Preamble

This code commentary is included in the R code itself and can be rendered at any stage using rmarkdown::render ("/Users/paul/Documents/CU_combined/Github/500_83_get_mixed_effect_model_results.R", clean = TRUE, output_format = "html_notebook"). Please check the session info at the end of the document for further notes on the coding environment.

Environment preparation

Empty buffer.

rm(list=ls())

Load Packages

library ("tidyverse") # dplyr and friends
library ("ggplot2")   # for ggCaterpillar
library ("gdata")     # matrix functions
library ("reshape2")  # melting
library ("lme4")      # mixed effect model
library ("sjPlot")    # mixed effect model - with plotting
library ("cowplot")   # exporting ggplots
library ("formula.tools") # better formatting of formulas
library ("stringr")    # better string concatenation
library ("magrittr")  # back-piping (only used for type conversion)

Functions

# Loaded from helper script:
source("/Users/paul/Documents/CU_combined/Github/500_00_functions.R")

“Not in” function

`%!in%` = Negate(`%in%`)

Function to subset data to fit model variables. Currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.

match_data_to_formula <- function (formula_item, data_item){
  
  # package loading
  require ("tidyverse")
  
  # message
  message("\nData is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.")
  
  # Setting types
  #   for debugging only
  # print(head(data_item))
  
  message("- Setting types.")
  cols <- c("PORT", "DEST", "ECO_PORT", "ECO_DEST", "ECO_DIFF")
  data_item[cols] <- lapply(data_item[cols], as.factor)  

  #   for debugging only
  # print(head(data_item))
  
        
  # remove superflous columns
  vars_to_keep <- all.vars (formula_item)

  message("- Input dimensions are: ", paste0( (dim(data_item)), " "),  ".")
  message("- Removed variables are: ", paste0( names(data_item)[which(names(data_item) %!in% vars_to_keep)], " "), ".")
  message("- Kept variables are: ", paste0(vars_to_keep, " "), ".")
  
  data_item <- data_item %>% select(all_of(vars_to_keep))

  message("- Intermediate dimensions are: ", paste0( (dim(data_item)), " "), ".")
  
  # remove superflous rows
  message("- Undefined rows have been removed, assuming they were real \"NA\" and not \"0\".")
  
  data_item <- data_item %>% filter(complete.cases(.))
  
  message("- Final dimensions are: ", paste0( (dim(data_item)), " "), ".")
  
  # return table object suitable for modelling with model formula
  return(data_item)

}

Calculate random effect model results

calculate_model <- function(formula_item, data_item){
  
  message("\nModelling function received variables: ", paste0(names(data_item) , " "), ".")
  message("   ... dimensions: ", paste0( (dim(data_item)), " "), ".")
  message("   ... formula: ", paste0(formula_item , " "), "." )
  
  model <- lmer(formula_item, data = data_item, REML=FALSE)

  return(model)
}

Model definitions

Define full models

following https://stackoverflow.com/questions/25312818/using-lapply-to-fit-multiple-model-how-to-keep-the-model-formula-self-contain

full_formulae <- list(
  
  # Original by Paul 
  as.formula(RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)),
  
  # as per email 04.02.2020
  # Unifrac ~ VOY_FREQ + env similarity + ecoregion + random port effects
  as.formula(RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)),
   
  # Unifrac ~ B_FON_NOECO + env similarity + ecoregion + random port effects
  as.formula(RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)),
  
  # Unifrac ~ B_HON_NOECO + env similarity + ecoregion + random port effects
  as.formula(RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST))
)

Define null models

For Anova comparison. Order must be the same as in list full_models.

null_formulae <- list(
  
  # Original by Paul 
  as.formula(RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)),
  
  # as per email 04.02.2020
  # Unifrac ~ VOY_FREQ + env similarity + ecoregion + random port effects
  as.formula(RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)),
   
  # Unifrac ~ B_FON_NOECO + env similarity + ecoregion + random port effects
  as.formula(RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)),
  
  # Unifrac ~ B_HON_NOECO + env similarity + ecoregion + random port effects
  as.formula(RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST))
)

Read in and format data

Please refer to project README.md file for further details on previous processing steps (dated 31-Jan-2020).

# define file path components for listing 
model_input_folder <- "/Users/paul/Documents/CU_combined/Zenodo/Results"
model_input_pattern <- glob2rx("??_results_euk_*_model_data_*.csv")

# read all file into lists for `lapply()` usage
model_input_files <- list.files(path=model_input_folder, 
  pattern = model_input_pattern, full.names = TRUE)

# store all tables in list and save input filenames alongside - skipping "X1" 
#  in case previous tables have column numbers, which they should not have anymore.
model_input_data <- suppressWarnings(lapply(model_input_files, 
  function(listed_file)  read_csv(listed_file, col_types = cols('X1' = col_skip()))))
names(model_input_data) <- model_input_files

Obtaining modelling results

Initialize results table

So that it can be filled in the loop.

analysis_summaries <- expand.grid(seq(model_input_data), seq(full_formulae))
analysis_summaries <- as_tibble(analysis_summaries)
analysis_summaries <- setNames(analysis_summaries, c("DIDX", "FIDX"))
analysis_summaries <- analysis_summaries %>% add_column(AKAI = 0, PVAL = 0, FRML = 0, DATA = 0)

analysis_summaries$AKAI  %<>% as.double
analysis_summaries$DATA %<>% as.character
analysis_summaries$FRML  %<>% as.character
analysis_summaries$PVAL  %<>% as.double

# use this approach to get around the loop - later
#   define all possible combinations for mapply call
#   for later - starting point
#   analysis_combinations <- expand.grid(seq(model_input_data), seq(full_formulae))
#   setNames(analysis_combinations, c("model_index", "formula_index"))
#   for later - starting point
#   list(model_input_data, full_formulae)

Calculating Results

Initially using loops, for sanity reasons. While looping fill results table analysis_summaries. Check raw model outputs below for Writing above results to results table row: n and look up n in both results tables all the way at the end of this page.

# loop over formulae
for (i in seq(full_formulae)){
  
  # loop over dat sets
  for (j in seq(model_input_data)){
  
    message("°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ ")
    message("\nStarting new analysis, with data index DIDX \"", j , "\" and formula index FIDX \"", i, "\" in Summary Tables." ) 
    message("Using formula: ", as.character(full_formulae[[i]]), " with data: ", as.character(basename(names(model_input_data)[[j]])), ". ")

    # define current model formula for parsing
    full_formula <- full_formulae[[i]]
    null_formula <- null_formulae[[i]]
     
    # define current data table for subsetting
    model_data_raw <- model_input_data[[j]]
         
    # match input table dimensions to current model formulae
    model_data <- match_data_to_formula(full_formula, model_data_raw)
    print(model_data, n = Inf)
  
    # calculate full model
    full_model <- calculate_model(full_formula, model_data)
     
    # calculate null model
    null_model <- calculate_model(null_formula, model_data)
     
    # print model summary and evaluations
    message("\nGetting Model Summary: ")
    sm <- summary(full_model)
    print(sm)
    message("\nGetting Model Coefficients from Summary: ")
    print(sm$coefficients)
      
    message("\nGetting Model ANOVA: ")
    an <- try(anova(null_model, full_model))
    try(print(an))

    # plot model coefficients
    message("\nPlotting Model Coefficients: ")
    plot <- plot_model(full_model, show.values = TRUE, value.offset = .3,
     type = "std", 
     title = paste("Coefficients for formula \"", as.character(full_formula),
     "\" and variables \"", str_c(names(model_data), collapse = "\", \""),"\" of input file: \"",
    basename(names(model_input_data)[[j]]), "\"." ))
  
    print(plot)
  
    # gather results
    #   set current row of results table
    crnt_row <- intersect(which(analysis_summaries$DIDX == j), which(analysis_summaries$FIDX == i))
    # message("Writing above results to results table row (but the table is re-sorted): ", crnt_row)
  
    #    fill results table
    analysis_summaries[crnt_row, ]$AKAI <- extractAIC(full_model)[2]
    analysis_summaries[crnt_row, ]$DATA <- as.character(basename(names(model_input_data)[[j]]))
    analysis_summaries[crnt_row, ]$FRML <- as.character(full_formulae[[i]])
    analysis_summaries[crnt_row, ]$PVAL <- an[2,8]
  
    # keep in mind for further elements from anova object:
    #  > str(an)
    #  Classes ‘anova’ and 'data.frame':    2 obs. of  8 variables:
    #  $ Df        : num  6 7
    #  $ AIC       : num  -158 -159
    #  $ BIC       : num  -145 -144
    #  $ logLik    : num  84.8 86.5
    #  $ deviance  : num  -170 -173
    #  $ Chisq     : num  NA 3.49
    #  $ Chi Df    : num  NA 1
    #  $ Pr(>Chisq): num  NA 0.0617

  }
}
°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ 

Starting new analysis, with data index DIDX "1" and formula index FIDX "1" in Summary Tables.
Using formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 01_results_euk_asv00_deep_UNIF_model_data_2020-Jan-31-14-15-13_no_ph_with_hon_info.csv. 

Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.
- Setting types.
- Input dimensions are: 64 17 .
- Removed variables are: ECO_PORT ECO_DEST VOY_FREQ B_FON_NOECO B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .
- Kept variables are: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST .
- Intermediate dimensions are: 64 6 .
- Undefined rows have been removed, assuming they were real "NA" and not "0".
- Final dimensions are: 64 6 .
# A tibble: 64 x 6
   RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT  DEST 
          <dbl>      <dbl>    <dbl> <fct>    <fct> <fct>
 1        0.836         10   1.45   TRUE     AD    BT   
 2        0.924          6   1.96   TRUE     AD    HT   
 3        0.880          2   2.15   TRUE     AD    WL   
 4        0.700         15   1.06   TRUE     AW    WL   
 5        0.636        189   1.56   TRUE     BT    AW   
 6        0.695         20   1.50   TRUE     BT    GH   
 7        0.818         11   2.92   TRUE     BT    HN   
 8        0.794        287   1.52   FALSE    BT    HT   
 9        0.785         26   2.14   TRUE     BT    LB   
10        0.756         75   3.16   FALSE    BT    MI   
11        0.794        221   1.56   FALSE    BT    NO   
12        0.690          6   1.50   TRUE     BT    OK   
13        0.772         17   1.41   TRUE     BT    PL   
14        0.665          5   1.49   TRUE     BT    RC   
15        0.732         83   1.57   TRUE     BT    RT   
16        0.723        180   0.921  FALSE    BT    WL   
17        0.776         94   2.25   TRUE     BT    ZB   
18        0.832         22   1.29   FALSE    CB    PL   
19        0.683         11   0.547  FALSE    CB    RC   
20        0.782          2   1.22   TRUE     CB    RT   
21        0.654         11   1.07   TRUE     GH    WL   
22        0.739         30   2.79   TRUE     HN    CB   
23        0.829         30   2.81   TRUE     HN    HT   
24        0.742          7   0.657  TRUE     HN    MI   
25        0.774        316   2.11   TRUE     HT    AW   
26        0.747         44   2.09   TRUE     HT    GH   
27        0.885         93   2.53   TRUE     HT    LB   
28        0.845        429   2.94   FALSE    HT    MI   
29        0.628       3937   0.0459 FALSE    HT    NO   
30        0.819          3   1.58   TRUE     HT    OK   
31        0.639         21   1.88   TRUE     HT    PL   
32        0.828          4   2.74   TRUE     HT    PM   
33        0.824         37   1.88   TRUE     HT    RC   
34        0.695        498   2.23   TRUE     HT    RT   
35        0.639         31   1.55   FALSE    HT    WL   
36        0.869         16   3.39   TRUE     HT    ZB   
37        0.738         74   1.94   FALSE    LB    CB   
38        0.726         11   1.50   TRUE     LB    MI   
39        0.844          3   2.92   TRUE     LB    WL   
40        0.748        114   4.15   TRUE     MI    AW   
41        0.864        185   2.94   FALSE    MI    NO   
42        0.712          8   3.38   TRUE     MI    OK   
43        0.786         44   4.18   TRUE     MI    RT   
44        0.799          2   3.49   TRUE     MI    ZB   
45        0.650         11   1.59   FALSE    NO    WL   
46        0.603          8   1.12   TRUE     RT    WL   
47        0.815        556   2.06   TRUE     SI    AD   
48        0.740        622   4.01   TRUE     SI    AW   
49        0.748        142   3.18   TRUE     SI    BT   
50        0.724         18   3.18   TRUE     SI    CB   
51        0.789         77   3.93   TRUE     SI    GH   
52        0.762        182   0.576  TRUE     SI    HN   
53        0.836        435   2.81   TRUE     SI    HT   
54        0.730        395   1.55   TRUE     SI    LB   
55        0.688         24   0.506  TRUE     SI    MI   
56        0.853        383   2.80   TRUE     SI    NO   
57        0.692        126   3.17   TRUE     SI    OK   
58        0.835        112   3.86   TRUE     SI    PL   
59        0.780         13   2.54   TRUE     SI    PM   
60        0.691         60   2.94   TRUE     SI    RC   
61        0.774       1055   4.05   TRUE     SI    RT   
62        0.798         12   3.84   TRUE     SI    WL   
63        0.789        207   3.51   TRUE     SI    ZB   
64        0.817          6   2.77   TRUE     WL    ZB   

Modelling function received variables: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 64 6 .
   ... formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .

Modelling function received variables: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 64 6 .
   ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .

Getting Model Summary: 
Linear mixed model fit by maximum likelihood  ['lmerMod']
Formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) +      (1 | DEST)
   Data: data_item

     AIC      BIC   logLik deviance df.resid 
  -159.1   -143.9     86.5   -173.1       57 

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-2.68550 -0.56009  0.01728  0.63958  1.55677 

Random effects:
 Groups   Name        Variance Std.Dev.
 DEST     (Intercept) 0.001079 0.03285 
 PORT     (Intercept) 0.001352 0.03677 
 Residual             0.002530 0.05030 
Number of obs: 64, groups:  DEST, 17; PORT, 13

Fixed effects:
               Estimate Std. Error t value
(Intercept)   7.022e-01  2.936e-02  23.917
PRED_TRIPS   -3.087e-05  1.531e-05  -2.016
PRED_ENV      3.222e-02  8.089e-03   3.983
ECO_DIFFTRUE -2.098e-03  2.103e-02  -0.100

Correlation of Fixed Effects:
            (Intr) PRED_T PRED_E
PRED_TRIPS  -0.338              
PRED_ENV    -0.565  0.216       
ECO_DIFFTRU -0.594  0.249 -0.030

Getting Model Coefficients from Summary: 
                  Estimate   Std. Error     t value
(Intercept)   0.7021552235 2.935840e-02 23.91667141
PRED_TRIPS   -0.0000308742 1.531312e-05 -2.01619301
PRED_ENV      0.0322170601 8.088774e-03  3.98293467
ECO_DIFFTRUE -0.0020984868 2.103275e-02 -0.09977234

Getting Model ANOVA: 
Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + 
full_model:     (1 | DEST)
           Df     AIC     BIC logLik deviance  Chisq Chi Df Pr(>Chisq)  
null_model  6 -157.57 -144.61 84.783  -169.57                           
full_model  7 -159.06 -143.94 86.528  -173.06 3.4905      1    0.06172 .
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Plotting Model Coefficients: 
°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ 

Starting new analysis, with data index DIDX "2" and formula index FIDX "1" in Summary Tables.
Using formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 01_results_euk_asv00_deep_UNIF_model_data_2020-Jan-31-14-15-13_with_hon_info.csv. 

Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.
- Setting types.
- Input dimensions are: 69 17 .
- Removed variables are: ECO_PORT ECO_DEST VOY_FREQ B_FON_NOECO B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .
- Kept variables are: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST .
- Intermediate dimensions are: 69 6 .
- Undefined rows have been removed, assuming they were real "NA" and not "0".
- Final dimensions are: 69 6 .
# A tibble: 69 x 6
   RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT  DEST 
          <dbl>      <dbl>    <dbl> <fct>    <fct> <fct>
 1        0.836         10   1.45   TRUE     AD    BT   
 2        0.924          6   1.96   TRUE     AD    HT   
 3        0.880          2   2.15   TRUE     AD    WL   
 4        0.700         15   1.06   TRUE     AW    WL   
 5        0.636        189   1.56   TRUE     BT    AW   
 6        0.695         20   1.50   TRUE     BT    GH   
 7        0.818         11   2.92   TRUE     BT    HN   
 8        0.794        287   1.52   FALSE    BT    HT   
 9        0.785         26   2.14   TRUE     BT    LB   
10        0.756         75   3.16   FALSE    BT    MI   
11        0.794        221   1.56   FALSE    BT    NO   
12        0.690          6   1.50   TRUE     BT    OK   
13        0.772         17   1.41   TRUE     BT    PL   
14        0.665          5   1.49   TRUE     BT    RC   
15        0.732         83   1.57   TRUE     BT    RT   
16        0.723        180   0.921  FALSE    BT    WL   
17        0.776         94   2.25   TRUE     BT    ZB   
18        0.832         22   1.29   FALSE    CB    PL   
19        0.683         11   0.547  FALSE    CB    RC   
20        0.782          2   1.22   TRUE     CB    RT   
21        0.654         11   1.07   TRUE     GH    WL   
22        0.739         30   2.79   TRUE     HN    CB   
23        0.829         30   2.81   TRUE     HN    HT   
24        0.742          7   0.657  TRUE     HN    MI   
25        0.774        316   2.11   TRUE     HT    AW   
26        0.747         44   2.09   TRUE     HT    GH   
27        0.885         93   2.53   TRUE     HT    LB   
28        0.845        429   2.94   FALSE    HT    MI   
29        0.628       3937   0.0459 FALSE    HT    NO   
30        0.819          3   1.58   TRUE     HT    OK   
31        0.639         21   1.88   TRUE     HT    PL   
32        0.828          4   2.74   TRUE     HT    PM   
33        0.824         37   1.88   TRUE     HT    RC   
34        0.695        498   2.23   TRUE     HT    RT   
35        0.639         31   1.55   FALSE    HT    WL   
36        0.869         16   3.39   TRUE     HT    ZB   
37        0.738         74   1.94   FALSE    LB    CB   
38        0.726         11   1.50   TRUE     LB    MI   
39        0.844          3   2.92   TRUE     LB    WL   
40        0.748        114   4.15   TRUE     MI    AW   
41        0.864        185   2.94   FALSE    MI    NO   
42        0.712          8   3.38   TRUE     MI    OK   
43        0.786         44   4.18   TRUE     MI    RT   
44        0.799          2   3.49   TRUE     MI    ZB   
45        0.650         11   1.59   FALSE    NO    WL   
46        0.775          0   2.92   TRUE     PH    BT   
47        0.700          0   2.79   TRUE     PH    CB   
48        0.861          0   2.81   TRUE     PH    HT   
49        0.658          0   0.657  TRUE     PH    MI   
50        0.706          0   0.576  TRUE     PH    SI   
51        0.603          8   1.12   TRUE     RT    WL   
52        0.815        556   2.06   TRUE     SI    AD   
53        0.740        622   4.01   TRUE     SI    AW   
54        0.748        142   3.18   TRUE     SI    BT   
55        0.724         18   3.18   TRUE     SI    CB   
56        0.789         77   3.93   TRUE     SI    GH   
57        0.762        182   0.576  TRUE     SI    HN   
58        0.836        435   2.81   TRUE     SI    HT   
59        0.730        395   1.55   TRUE     SI    LB   
60        0.688         24   0.506  TRUE     SI    MI   
61        0.853        383   2.80   TRUE     SI    NO   
62        0.692        126   3.17   TRUE     SI    OK   
63        0.835        112   3.86   TRUE     SI    PL   
64        0.780         13   2.54   TRUE     SI    PM   
65        0.691         60   2.94   TRUE     SI    RC   
66        0.774       1055   4.05   TRUE     SI    RT   
67        0.798         12   3.84   TRUE     SI    WL   
68        0.789        207   3.51   TRUE     SI    ZB   
69        0.817          6   2.77   TRUE     WL    ZB   

Modelling function received variables: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 69 6 .
   ... formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .

Modelling function received variables: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 69 6 .
   ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .

Getting Model Summary: 
Linear mixed model fit by maximum likelihood  ['lmerMod']
Formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) +      (1 | DEST)
   Data: data_item

     AIC      BIC   logLik deviance df.resid 
  -175.5   -159.8     94.7   -189.5       62 

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-2.79058 -0.51699  0.02379  0.61577  1.65245 

Random effects:
 Groups   Name        Variance Std.Dev.
 DEST     (Intercept) 0.001230 0.03507 
 PORT     (Intercept) 0.001292 0.03594 
 Residual             0.002343 0.04840 
Number of obs: 69, groups:  DEST, 18; PORT, 14

Fixed effects:
               Estimate Std. Error t value
(Intercept)   6.996e-01  2.816e-02  24.850
PRED_TRIPS   -3.223e-05  1.484e-05  -2.172
PRED_ENV      3.351e-02  7.480e-03   4.480
ECO_DIFFTRUE -3.811e-03  2.031e-02  -0.188

Correlation of Fixed Effects:
            (Intr) PRED_T PRED_E
PRED_TRIPS  -0.328              
PRED_ENV    -0.540  0.209       
ECO_DIFFTRU -0.623  0.247 -0.019

Getting Model Coefficients from Summary: 
                  Estimate   Std. Error    t value
(Intercept)   6.996488e-01 2.815538e-02 24.8495593
PRED_TRIPS   -3.222894e-05 1.483686e-05 -2.1722204
PRED_ENV      3.350743e-02 7.480161e-03  4.4795066
ECO_DIFFTRUE -3.810566e-03 2.030875e-02 -0.1876317

Getting Model ANOVA: 
Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + 
full_model:     (1 | DEST)
           Df     AIC     BIC logLik deviance  Chisq Chi Df Pr(>Chisq)  
null_model  6 -173.39 -159.99 92.697  -185.39                           
full_model  7 -175.49 -159.85 94.743  -189.49 4.0929      1    0.04306 *
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Plotting Model Coefficients: 

°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ 

Starting new analysis, with data index DIDX "3" and formula index FIDX "1" in Summary Tables.
Using formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 02_results_euk_otu99_deep_UNIF_model_data_2020-Jan-31-14-15-28_no_ph_with_hon_info.csv. 

Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.
- Setting types.
- Input dimensions are: 64 17 .
- Removed variables are: ECO_PORT ECO_DEST VOY_FREQ B_FON_NOECO B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .
- Kept variables are: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST .
- Intermediate dimensions are: 64 6 .
- Undefined rows have been removed, assuming they were real "NA" and not "0".
- Final dimensions are: 64 6 .
# A tibble: 64 x 6
   RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT  DEST 
          <dbl>      <dbl>    <dbl> <fct>    <fct> <fct>
 1        0.836         10   1.45   TRUE     AD    BT   
 2        0.924          6   1.96   TRUE     AD    HT   
 3        0.880          2   2.15   TRUE     AD    WL   
 4        0.708         15   1.06   TRUE     AW    WL   
 5        0.634        189   1.56   TRUE     BT    AW   
 6        0.708         20   1.50   TRUE     BT    GH   
 7        0.816         11   2.92   TRUE     BT    HN   
 8        0.800        287   1.52   FALSE    BT    HT   
 9        0.791         26   2.14   TRUE     BT    LB   
10        0.751         75   3.16   FALSE    BT    MI   
11        0.802        221   1.56   FALSE    BT    NO   
12        0.702          6   1.50   TRUE     BT    OK   
13        0.784         17   1.41   TRUE     BT    PL   
14        0.688          5   1.49   TRUE     BT    RC   
15        0.732         83   1.57   TRUE     BT    RT   
16        0.740        180   0.921  FALSE    BT    WL   
17        0.786         94   2.25   TRUE     BT    ZB   
18        0.831         22   1.29   FALSE    CB    PL   
19        0.688         11   0.547  FALSE    CB    RC   
20        0.780          2   1.22   TRUE     CB    RT   
21        0.671         11   1.07   TRUE     GH    WL   
22        0.736         30   2.79   TRUE     HN    CB   
23        0.830         30   2.81   TRUE     HN    HT   
24        0.743          7   0.657  TRUE     HN    MI   
25        0.781        316   2.11   TRUE     HT    AW   
26        0.753         44   2.09   TRUE     HT    GH   
27        0.892         93   2.53   TRUE     HT    LB   
28        0.851        429   2.94   FALSE    HT    MI   
29        0.635       3937   0.0459 FALSE    HT    NO   
30        0.837          3   1.58   TRUE     HT    OK   
31        0.654         21   1.88   TRUE     HT    PL   
32        0.824          4   2.74   TRUE     HT    PM   
33        0.845         37   1.88   TRUE     HT    RC   
34        0.700        498   2.23   TRUE     HT    RT   
35        0.644         31   1.55   FALSE    HT    WL   
36        0.879         16   3.39   TRUE     HT    ZB   
37        0.730         74   1.94   FALSE    LB    CB   
38        0.726         11   1.50   TRUE     LB    MI   
39        0.851          3   2.92   TRUE     LB    WL   
40        0.747        114   4.15   TRUE     MI    AW   
41        0.870        185   2.94   FALSE    MI    NO   
42        0.715          8   3.38   TRUE     MI    OK   
43        0.789         44   4.18   TRUE     MI    RT   
44        0.802          2   3.49   TRUE     MI    ZB   
45        0.653         11   1.59   FALSE    NO    WL   
46        0.607          8   1.12   TRUE     RT    WL   
47        0.815        556   2.06   TRUE     SI    AD   
48        0.737        622   4.01   TRUE     SI    AW   
49        0.747        142   3.18   TRUE     SI    BT   
50        0.724         18   3.18   TRUE     SI    CB   
51        0.790         77   3.93   TRUE     SI    GH   
52        0.768        182   0.576  TRUE     SI    HN   
53        0.837        435   2.81   TRUE     SI    HT   
54        0.732        395   1.55   TRUE     SI    LB   
55        0.703         24   0.506  TRUE     SI    MI   
56        0.851        383   2.80   TRUE     SI    NO   
57        0.698        126   3.17   TRUE     SI    OK   
58        0.836        112   3.86   TRUE     SI    PL   
59        0.780         13   2.54   TRUE     SI    PM   
60        0.702         60   2.94   TRUE     SI    RC   
61        0.774       1055   4.05   TRUE     SI    RT   
62        0.801         12   3.84   TRUE     SI    WL   
63        0.786        207   3.51   TRUE     SI    ZB   
64        0.821          6   2.77   TRUE     WL    ZB   

Modelling function received variables: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 64 6 .
   ... formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .

Modelling function received variables: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 64 6 .
   ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .

Getting Model Summary: 
Linear mixed model fit by maximum likelihood  ['lmerMod']
Formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) +      (1 | DEST)
   Data: data_item

     AIC      BIC   logLik deviance df.resid 
  -158.6   -143.5     86.3   -172.6       57 

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-2.48872 -0.60199  0.02656  0.63185  1.59683 

Random effects:
 Groups   Name        Variance  Std.Dev.
 DEST     (Intercept) 0.0009423 0.03070 
 PORT     (Intercept) 0.0010344 0.03216 
 Residual             0.0027172 0.05213 
Number of obs: 64, groups:  DEST, 17; PORT, 13

Fixed effects:
               Estimate Std. Error t value
(Intercept)   7.106e-01  2.886e-02  24.618
PRED_TRIPS   -3.083e-05  1.562e-05  -1.974
PRED_ENV      2.898e-02  8.178e-03   3.544
ECO_DIFFTRUE  2.099e-04  2.120e-02   0.010

Correlation of Fixed Effects:
            (Intr) PRED_T PRED_E
PRED_TRIPS  -0.344              
PRED_ENV    -0.579  0.207       
ECO_DIFFTRU -0.595  0.246 -0.047

Getting Model Coefficients from Summary: 
                  Estimate   Std. Error      t value
(Intercept)   7.105561e-01 2.886310e-02 24.618149077
PRED_TRIPS   -3.083354e-05 1.562214e-05 -1.973708071
PRED_ENV      2.898454e-02 8.177957e-03  3.544228017
ECO_DIFFTRUE  2.098470e-04 2.119563e-02  0.009900486

Getting Model ANOVA: 
Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + 
full_model:     (1 | DEST)
           Df     AIC     BIC logLik deviance  Chisq Chi Df Pr(>Chisq)  
null_model  6 -157.29 -144.34 84.645  -169.29                           
full_model  7 -158.63 -143.52 86.315  -172.63 3.3413      1    0.06756 .
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Plotting Model Coefficients: 

°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ 

Starting new analysis, with data index DIDX "4" and formula index FIDX "1" in Summary Tables.
Using formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 02_results_euk_otu99_deep_UNIF_model_data_2020-Jan-31-14-15-28_with_hon_info.csv. 

Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.
- Setting types.
- Input dimensions are: 69 17 .
- Removed variables are: ECO_PORT ECO_DEST VOY_FREQ B_FON_NOECO B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .
- Kept variables are: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST .
- Intermediate dimensions are: 69 6 .
- Undefined rows have been removed, assuming they were real "NA" and not "0".
- Final dimensions are: 69 6 .
# A tibble: 69 x 6
   RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT  DEST 
          <dbl>      <dbl>    <dbl> <fct>    <fct> <fct>
 1        0.836         10   1.45   TRUE     AD    BT   
 2        0.924          6   1.96   TRUE     AD    HT   
 3        0.880          2   2.15   TRUE     AD    WL   
 4        0.708         15   1.06   TRUE     AW    WL   
 5        0.634        189   1.56   TRUE     BT    AW   
 6        0.708         20   1.50   TRUE     BT    GH   
 7        0.816         11   2.92   TRUE     BT    HN   
 8        0.800        287   1.52   FALSE    BT    HT   
 9        0.791         26   2.14   TRUE     BT    LB   
10        0.751         75   3.16   FALSE    BT    MI   
11        0.802        221   1.56   FALSE    BT    NO   
12        0.702          6   1.50   TRUE     BT    OK   
13        0.784         17   1.41   TRUE     BT    PL   
14        0.688          5   1.49   TRUE     BT    RC   
15        0.732         83   1.57   TRUE     BT    RT   
16        0.740        180   0.921  FALSE    BT    WL   
17        0.786         94   2.25   TRUE     BT    ZB   
18        0.831         22   1.29   FALSE    CB    PL   
19        0.688         11   0.547  FALSE    CB    RC   
20        0.780          2   1.22   TRUE     CB    RT   
21        0.671         11   1.07   TRUE     GH    WL   
22        0.736         30   2.79   TRUE     HN    CB   
23        0.830         30   2.81   TRUE     HN    HT   
24        0.743          7   0.657  TRUE     HN    MI   
25        0.781        316   2.11   TRUE     HT    AW   
26        0.753         44   2.09   TRUE     HT    GH   
27        0.892         93   2.53   TRUE     HT    LB   
28        0.851        429   2.94   FALSE    HT    MI   
29        0.635       3937   0.0459 FALSE    HT    NO   
30        0.837          3   1.58   TRUE     HT    OK   
31        0.654         21   1.88   TRUE     HT    PL   
32        0.824          4   2.74   TRUE     HT    PM   
33        0.845         37   1.88   TRUE     HT    RC   
34        0.700        498   2.23   TRUE     HT    RT   
35        0.644         31   1.55   FALSE    HT    WL   
36        0.879         16   3.39   TRUE     HT    ZB   
37        0.730         74   1.94   FALSE    LB    CB   
38        0.726         11   1.50   TRUE     LB    MI   
39        0.851          3   2.92   TRUE     LB    WL   
40        0.747        114   4.15   TRUE     MI    AW   
41        0.870        185   2.94   FALSE    MI    NO   
42        0.715          8   3.38   TRUE     MI    OK   
43        0.789         44   4.18   TRUE     MI    RT   
44        0.802          2   3.49   TRUE     MI    ZB   
45        0.653         11   1.59   FALSE    NO    WL   
46        0.771          0   2.92   TRUE     PH    BT   
47        0.700          0   2.79   TRUE     PH    CB   
48        0.863          0   2.81   TRUE     PH    HT   
49        0.657          0   0.657  TRUE     PH    MI   
50        0.714          0   0.576  TRUE     PH    SI   
51        0.607          8   1.12   TRUE     RT    WL   
52        0.815        556   2.06   TRUE     SI    AD   
53        0.737        622   4.01   TRUE     SI    AW   
54        0.747        142   3.18   TRUE     SI    BT   
55        0.724         18   3.18   TRUE     SI    CB   
56        0.790         77   3.93   TRUE     SI    GH   
57        0.768        182   0.576  TRUE     SI    HN   
58        0.837        435   2.81   TRUE     SI    HT   
59        0.732        395   1.55   TRUE     SI    LB   
60        0.703         24   0.506  TRUE     SI    MI   
61        0.851        383   2.80   TRUE     SI    NO   
62        0.698        126   3.17   TRUE     SI    OK   
63        0.836        112   3.86   TRUE     SI    PL   
64        0.780         13   2.54   TRUE     SI    PM   
65        0.702         60   2.94   TRUE     SI    RC   
66        0.774       1055   4.05   TRUE     SI    RT   
67        0.801         12   3.84   TRUE     SI    WL   
68        0.786        207   3.51   TRUE     SI    ZB   
69        0.821          6   2.77   TRUE     WL    ZB   

Modelling function received variables: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 69 6 .
   ... formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .

Modelling function received variables: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 69 6 .
   ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .

Getting Model Summary: 
Linear mixed model fit by maximum likelihood  ['lmerMod']
Formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) +      (1 | DEST)
   Data: data_item

     AIC      BIC   logLik deviance df.resid 
  -174.7   -159.0     94.3   -188.7       62 

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-2.59613 -0.55450  0.07882  0.59301  1.70025 

Random effects:
 Groups   Name        Variance Std.Dev.
 DEST     (Intercept) 0.001116 0.03341 
 PORT     (Intercept) 0.001042 0.03227 
 Residual             0.002506 0.05006 
Number of obs: 69, groups:  DEST, 18; PORT, 14

Fixed effects:
               Estimate Std. Error t value
(Intercept)   7.072e-01  2.788e-02  25.368
PRED_TRIPS   -3.231e-05  1.516e-05  -2.131
PRED_ENV      3.053e-02  7.580e-03   4.028
ECO_DIFFTRUE -1.682e-03  2.051e-02  -0.082

Correlation of Fixed Effects:
            (Intr) PRED_T PRED_E
PRED_TRIPS  -0.333              
PRED_ENV    -0.553  0.203       
ECO_DIFFTRU -0.626  0.246 -0.030

Getting Model Coefficients from Summary: 
                  Estimate   Std. Error     t value
(Intercept)   7.072436e-01 0.0278789750 25.36834883
PRED_TRIPS   -3.230581e-05 0.0000151578 -2.13129866
PRED_ENV      3.053376e-02 0.0075801692  4.02811070
ECO_DIFFTRUE -1.681521e-03 0.0205080908 -0.08199305

Getting Model ANOVA: 
Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + 
full_model:     (1 | DEST)
           Df     AIC     BIC logLik deviance Chisq Chi Df Pr(>Chisq)  
null_model  6 -172.74 -159.33 92.368  -184.74                          
full_model  7 -174.67 -159.03 94.334  -188.67 3.932      1    0.04738 *
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Plotting Model Coefficients: 

°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ 

Starting new analysis, with data index DIDX "5" and formula index FIDX "1" in Summary Tables.
Using formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 03_results_euk_asv00_deep_JAQU_model_data_2020-Jan-31-14-15-41_no_ph_with_hon_info.csv. 

Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.
- Setting types.
- Input dimensions are: 64 17 .
- Removed variables are: ECO_PORT ECO_DEST VOY_FREQ B_FON_NOECO B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .
- Kept variables are: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST .
- Intermediate dimensions are: 64 6 .
- Undefined rows have been removed, assuming they were real "NA" and not "0".
- Final dimensions are: 64 6 .
# A tibble: 64 x 6
   RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT  DEST 
          <dbl>      <dbl>    <dbl> <fct>    <fct> <fct>
 1        0.983         10   1.45   TRUE     AD    BT   
 2        1              6   1.96   TRUE     AD    HT   
 3        0.996          2   2.15   TRUE     AD    WL   
 4        0.950         15   1.06   TRUE     AW    WL   
 5        0.902        189   1.56   TRUE     BT    AW   
 6        0.942         20   1.50   TRUE     BT    GH   
 7        0.985         11   2.92   TRUE     BT    HN   
 8        0.989        287   1.52   FALSE    BT    HT   
 9        0.964         26   2.14   TRUE     BT    LB   
10        0.974         75   3.16   FALSE    BT    MI   
11        0.985        221   1.56   FALSE    BT    NO   
12        0.937          6   1.50   TRUE     BT    OK   
13        0.991         17   1.41   TRUE     BT    PL   
14        0.942          5   1.49   TRUE     BT    RC   
15        0.955         83   1.57   TRUE     BT    RT   
16        0.958        180   0.921  FALSE    BT    WL   
17        0.955         94   2.25   TRUE     BT    ZB   
18        1             22   1.29   FALSE    CB    PL   
19        0.908         11   0.547  FALSE    CB    RC   
20        0.989          2   1.22   TRUE     CB    RT   
21        0.913         11   1.07   TRUE     GH    WL   
22        0.980         30   2.79   TRUE     HN    CB   
23        0.993         30   2.81   TRUE     HN    HT   
24        0.940          7   0.657  TRUE     HN    MI   
25        0.988        316   2.11   TRUE     HT    AW   
26        0.969         44   2.09   TRUE     HT    GH   
27        1.00          93   2.53   TRUE     HT    LB   
28        0.994        429   2.94   FALSE    HT    MI   
29        0.892       3937   0.0459 FALSE    HT    NO   
30        0.998          3   1.58   TRUE     HT    OK   
31        0.916         21   1.88   TRUE     HT    PL   
32        0.997          4   2.74   TRUE     HT    PM   
33        0.997         37   1.88   TRUE     HT    RC   
34        0.948        498   2.23   TRUE     HT    RT   
35        0.906         31   1.55   FALSE    HT    WL   
36        0.999         16   3.39   TRUE     HT    ZB   
37        0.940         74   1.94   FALSE    LB    CB   
38        0.943         11   1.50   TRUE     LB    MI   
39        0.997          3   2.92   TRUE     LB    WL   
40        0.988        114   4.15   TRUE     MI    AW   
41        0.998        185   2.94   FALSE    MI    NO   
42        0.963          8   3.38   TRUE     MI    OK   
43        0.991         44   4.18   TRUE     MI    RT   
44        0.988          2   3.49   TRUE     MI    ZB   
45        0.902         11   1.59   FALSE    NO    WL   
46        0.894          8   1.12   TRUE     RT    WL   
47        0.971        556   2.06   TRUE     SI    AD   
48        0.985        622   4.01   TRUE     SI    AW   
49        0.973        142   3.18   TRUE     SI    BT   
50        0.981         18   3.18   TRUE     SI    CB   
51        0.995         77   3.93   TRUE     SI    GH   
52        0.959        182   0.576  TRUE     SI    HN   
53        0.997        435   2.81   TRUE     SI    HT   
54        0.967        395   1.55   TRUE     SI    LB   
55        0.926         24   0.506  TRUE     SI    MI   
56        0.997        383   2.80   TRUE     SI    NO   
57        0.958        126   3.17   TRUE     SI    OK   
58        0.998        112   3.86   TRUE     SI    PL   
59        0.996         13   2.54   TRUE     SI    PM   
60        0.965         60   2.94   TRUE     SI    RC   
61        0.992       1055   4.05   TRUE     SI    RT   
62        0.996         12   3.84   TRUE     SI    WL   
63        0.984        207   3.51   TRUE     SI    ZB   
64        0.996          6   2.77   TRUE     WL    ZB   

Modelling function received variables: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 64 6 .
   ... formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Modelling function received variables: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 64 6 .
   ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Getting Model Summary: 
Linear mixed model fit by maximum likelihood  ['lmerMod']
Formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) +      (1 | DEST)
   Data: data_item

     AIC      BIC   logLik deviance df.resid 
  -278.9   -263.8    146.4   -292.9       57 

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-2.20269 -0.58867 -0.03706  0.75178  1.96334 

Random effects:
 Groups   Name        Variance Std.Dev.
 DEST     (Intercept) 2.44e-05 0.00494 
 PORT     (Intercept) 0.00e+00 0.00000 
 Residual             5.80e-04 0.02408 
Number of obs: 64, groups:  DEST, 17; PORT, 13

Fixed effects:
               Estimate Std. Error t value
(Intercept)   9.293e-01  9.578e-03  97.025
PRED_TRIPS   -8.721e-06  6.273e-06  -1.390
PRED_ENV      1.732e-02  3.115e-03   5.559
ECO_DIFFTRUE  2.657e-03  8.361e-03   0.318

Correlation of Fixed Effects:
            (Intr) PRED_T PRED_E
PRED_TRIPS  -0.354              
PRED_ENV    -0.586  0.106       
ECO_DIFFTRU -0.574  0.217 -0.226
convergence code: 0
boundary (singular) fit: see ?isSingular

Getting Model Coefficients from Summary: 
                  Estimate   Std. Error   t value
(Intercept)   9.292775e-01 9.577734e-03 97.024780
PRED_TRIPS   -8.721379e-06 6.272946e-06 -1.390316
PRED_ENV      1.731631e-02 3.115141e-03  5.558757
ECO_DIFFTRUE  2.657274e-03 8.361335e-03  0.317805

Getting Model ANOVA: 
Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + 
full_model:     (1 | DEST)
           Df     AIC     BIC logLik deviance  Chisq Chi Df Pr(>Chisq)
null_model  6 -279.16 -266.20 145.58  -291.16                         
full_model  7 -278.86 -263.75 146.43  -292.86 1.7066      1     0.1914

Plotting Model Coefficients: 
boundary (singular) fit: see ?isSingular

°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ 

Starting new analysis, with data index DIDX "6" and formula index FIDX "1" in Summary Tables.
Using formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 03_results_euk_asv00_deep_JAQU_model_data_2020-Jan-31-14-15-41_with_hon_info.csv. 

Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.
- Setting types.
- Input dimensions are: 69 17 .
- Removed variables are: ECO_PORT ECO_DEST VOY_FREQ B_FON_NOECO B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .
- Kept variables are: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST .
- Intermediate dimensions are: 69 6 .
- Undefined rows have been removed, assuming they were real "NA" and not "0".
- Final dimensions are: 69 6 .
# A tibble: 69 x 6
   RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT  DEST 
          <dbl>      <dbl>    <dbl> <fct>    <fct> <fct>
 1        0.983         10   1.45   TRUE     AD    BT   
 2        1              6   1.96   TRUE     AD    HT   
 3        0.996          2   2.15   TRUE     AD    WL   
 4        0.950         15   1.06   TRUE     AW    WL   
 5        0.902        189   1.56   TRUE     BT    AW   
 6        0.942         20   1.50   TRUE     BT    GH   
 7        0.985         11   2.92   TRUE     BT    HN   
 8        0.989        287   1.52   FALSE    BT    HT   
 9        0.964         26   2.14   TRUE     BT    LB   
10        0.974         75   3.16   FALSE    BT    MI   
11        0.985        221   1.56   FALSE    BT    NO   
12        0.937          6   1.50   TRUE     BT    OK   
13        0.991         17   1.41   TRUE     BT    PL   
14        0.942          5   1.49   TRUE     BT    RC   
15        0.955         83   1.57   TRUE     BT    RT   
16        0.958        180   0.921  FALSE    BT    WL   
17        0.955         94   2.25   TRUE     BT    ZB   
18        1             22   1.29   FALSE    CB    PL   
19        0.908         11   0.547  FALSE    CB    RC   
20        0.989          2   1.22   TRUE     CB    RT   
21        0.913         11   1.07   TRUE     GH    WL   
22        0.980         30   2.79   TRUE     HN    CB   
23        0.993         30   2.81   TRUE     HN    HT   
24        0.940          7   0.657  TRUE     HN    MI   
25        0.988        316   2.11   TRUE     HT    AW   
26        0.969         44   2.09   TRUE     HT    GH   
27        1.00          93   2.53   TRUE     HT    LB   
28        0.994        429   2.94   FALSE    HT    MI   
29        0.892       3937   0.0459 FALSE    HT    NO   
30        0.998          3   1.58   TRUE     HT    OK   
31        0.916         21   1.88   TRUE     HT    PL   
32        0.997          4   2.74   TRUE     HT    PM   
33        0.997         37   1.88   TRUE     HT    RC   
34        0.948        498   2.23   TRUE     HT    RT   
35        0.906         31   1.55   FALSE    HT    WL   
36        0.999         16   3.39   TRUE     HT    ZB   
37        0.940         74   1.94   FALSE    LB    CB   
38        0.943         11   1.50   TRUE     LB    MI   
39        0.997          3   2.92   TRUE     LB    WL   
40        0.988        114   4.15   TRUE     MI    AW   
41        0.998        185   2.94   FALSE    MI    NO   
42        0.963          8   3.38   TRUE     MI    OK   
43        0.991         44   4.18   TRUE     MI    RT   
44        0.988          2   3.49   TRUE     MI    ZB   
45        0.902         11   1.59   FALSE    NO    WL   
46        0.974          0   2.92   TRUE     PH    BT   
47        0.973          0   2.79   TRUE     PH    CB   
48        0.999          0   2.81   TRUE     PH    HT   
49        0.910          0   0.657  TRUE     PH    MI   
50        0.931          0   0.576  TRUE     PH    SI   
51        0.894          8   1.12   TRUE     RT    WL   
52        0.971        556   2.06   TRUE     SI    AD   
53        0.985        622   4.01   TRUE     SI    AW   
54        0.973        142   3.18   TRUE     SI    BT   
55        0.981         18   3.18   TRUE     SI    CB   
56        0.995         77   3.93   TRUE     SI    GH   
57        0.959        182   0.576  TRUE     SI    HN   
58        0.997        435   2.81   TRUE     SI    HT   
59        0.967        395   1.55   TRUE     SI    LB   
60        0.926         24   0.506  TRUE     SI    MI   
61        0.997        383   2.80   TRUE     SI    NO   
62        0.958        126   3.17   TRUE     SI    OK   
63        0.998        112   3.86   TRUE     SI    PL   
64        0.996         13   2.54   TRUE     SI    PM   
65        0.965         60   2.94   TRUE     SI    RC   
66        0.992       1055   4.05   TRUE     SI    RT   
67        0.996         12   3.84   TRUE     SI    WL   
68        0.984        207   3.51   TRUE     SI    ZB   
69        0.996          6   2.77   TRUE     WL    ZB   

Modelling function received variables: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 69 6 .
   ... formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Modelling function received variables: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 69 6 .
   ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Getting Model Summary: 
Linear mixed model fit by maximum likelihood  ['lmerMod']
Formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) +      (1 | DEST)
   Data: data_item

     AIC      BIC   logLik deviance df.resid 
  -304.5   -288.9    159.3   -318.5       62 

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-2.16269 -0.57072 -0.08068  0.74398  2.00855 

Random effects:
 Groups   Name        Variance  Std.Dev.
 DEST     (Intercept) 3.202e-05 0.005659
 PORT     (Intercept) 0.000e+00 0.000000
 Residual             5.499e-04 0.023450
Number of obs: 69, groups:  DEST, 18; PORT, 14

Fixed effects:
               Estimate Std. Error t value
(Intercept)   9.277e-01  9.270e-03 100.076
PRED_TRIPS   -8.556e-06  6.130e-06  -1.396
PRED_ENV      1.830e-02  2.897e-03   6.318
ECO_DIFFTRUE  1.228e-03  8.085e-03   0.152

Correlation of Fixed Effects:
            (Intr) PRED_T PRED_E
PRED_TRIPS  -0.349              
PRED_ENV    -0.566  0.098       
ECO_DIFFTRU -0.615  0.229 -0.197
convergence code: 0
boundary (singular) fit: see ?isSingular

Getting Model Coefficients from Summary: 
                  Estimate   Std. Error     t value
(Intercept)   9.276890e-01 9.269852e-03 100.0759274
PRED_TRIPS   -8.555574e-06 6.129774e-06  -1.3957405
PRED_ENV      1.830313e-02 2.897061e-03   6.3178267
ECO_DIFFTRUE  1.228085e-03 8.085449e-03   0.1518882

Getting Model ANOVA: 
Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + 
full_model:     (1 | DEST)
           Df     AIC     BIC logLik deviance Chisq Chi Df Pr(>Chisq)
null_model  6 -304.77 -291.37 158.39  -316.77                        
full_model  7 -304.53 -288.89 159.26  -318.53 1.753      1     0.1855

Plotting Model Coefficients: 
boundary (singular) fit: see ?isSingular

°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ 

Starting new analysis, with data index DIDX "7" and formula index FIDX "1" in Summary Tables.
Using formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 04_results_euk_otu99_deep_JAQU_model_data_2020-Jan-31-14-15-52_no_ph_with_hon_info.csv. 

Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.
- Setting types.
- Input dimensions are: 64 17 .
- Removed variables are: ECO_PORT ECO_DEST VOY_FREQ B_FON_NOECO B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .
- Kept variables are: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST .
- Intermediate dimensions are: 64 6 .
- Undefined rows have been removed, assuming they were real "NA" and not "0".
- Final dimensions are: 64 6 .
# A tibble: 64 x 6
   RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT  DEST 
          <dbl>      <dbl>    <dbl> <fct>    <fct> <fct>
 1        0.955         10   1.45   TRUE     AD    BT   
 2        0.999          6   1.96   TRUE     AD    HT   
 3        0.984          2   2.15   TRUE     AD    WL   
 4        0.919         15   1.06   TRUE     AW    WL   
 5        0.848        189   1.56   TRUE     BT    AW   
 6        0.907         20   1.50   TRUE     BT    GH   
 7        0.973         11   2.92   TRUE     BT    HN   
 8        0.980        287   1.52   FALSE    BT    HT   
 9        0.938         26   2.14   TRUE     BT    LB   
10        0.934         75   3.16   FALSE    BT    MI   
11        0.971        221   1.56   FALSE    BT    NO   
12        0.888          6   1.50   TRUE     BT    OK   
13        0.982         17   1.41   TRUE     BT    PL   
14        0.904          5   1.49   TRUE     BT    RC   
15        0.928         83   1.57   TRUE     BT    RT   
16        0.935        180   0.921  FALSE    BT    WL   
17        0.932         94   2.25   TRUE     BT    ZB   
18        0.998         22   1.29   FALSE    CB    PL   
19        0.863         11   0.547  FALSE    CB    RC   
20        0.970          2   1.22   TRUE     CB    RT   
21        0.879         11   1.07   TRUE     GH    WL   
22        0.948         30   2.79   TRUE     HN    CB   
23        0.988         30   2.81   TRUE     HN    HT   
24        0.902          7   0.657  TRUE     HN    MI   
25        0.981        316   2.11   TRUE     HT    AW   
26        0.951         44   2.09   TRUE     HT    GH   
27        0.998         93   2.53   TRUE     HT    LB   
28        0.987        429   2.94   FALSE    HT    MI   
29        0.848       3937   0.0459 FALSE    HT    NO   
30        0.990          3   1.58   TRUE     HT    OK   
31        0.867         21   1.88   TRUE     HT    PL   
32        0.995          4   2.74   TRUE     HT    PM   
33        0.992         37   1.88   TRUE     HT    RC   
34        0.921        498   2.23   TRUE     HT    RT   
35        0.866         31   1.55   FALSE    HT    WL   
36        0.997         16   3.39   TRUE     HT    ZB   
37        0.902         74   1.94   FALSE    LB    CB   
38        0.908         11   1.50   TRUE     LB    MI   
39        0.991          3   2.92   TRUE     LB    WL   
40        0.967        114   4.15   TRUE     MI    AW   
41        0.990        185   2.94   FALSE    MI    NO   
42        0.928          8   3.38   TRUE     MI    OK   
43        0.977         44   4.18   TRUE     MI    RT   
44        0.966          2   3.49   TRUE     MI    ZB   
45        0.857         11   1.59   FALSE    NO    WL   
46        0.840          8   1.12   TRUE     RT    WL   
47        0.946        556   2.06   TRUE     SI    AD   
48        0.960        622   4.01   TRUE     SI    AW   
49        0.947        142   3.18   TRUE     SI    BT   
50        0.950         18   3.18   TRUE     SI    CB   
51        0.986         77   3.93   TRUE     SI    GH   
52        0.935        182   0.576  TRUE     SI    HN   
53        0.993        435   2.81   TRUE     SI    HT   
54        0.932        395   1.55   TRUE     SI    LB   
55        0.883         24   0.506  TRUE     SI    MI   
56        0.994        383   2.80   TRUE     SI    NO   
57        0.922        126   3.17   TRUE     SI    OK   
58        0.996        112   3.86   TRUE     SI    PL   
59        0.983         13   2.54   TRUE     SI    PM   
60        0.930         60   2.94   TRUE     SI    RC   
61        0.976       1055   4.05   TRUE     SI    RT   
62        0.986         12   3.84   TRUE     SI    WL   
63        0.952        207   3.51   TRUE     SI    ZB   
64        0.987          6   2.77   TRUE     WL    ZB   

Modelling function received variables: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 64 6 .
   ... formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Modelling function received variables: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 64 6 .
   ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Getting Model Summary: 
Linear mixed model fit by maximum likelihood  ['lmerMod']
Formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) +      (1 | DEST)
   Data: data_item

     AIC      BIC   logLik deviance df.resid 
  -225.8   -210.7    119.9   -239.8       57 

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.1578 -0.6235 -0.0341  0.7580  1.9540 

Random effects:
 Groups   Name        Variance  Std.Dev.
 DEST     (Intercept) 7.188e-05 0.008478
 PORT     (Intercept) 0.000e+00 0.000000
 Residual             1.316e-03 0.036276
Number of obs: 64, groups:  DEST, 17; PORT, 13

Fixed effects:
               Estimate Std. Error t value
(Intercept)   8.945e-01  1.456e-02  61.445
PRED_TRIPS   -1.115e-05  9.499e-06  -1.174
PRED_ENV      2.318e-02  4.707e-03   4.925
ECO_DIFFTRUE  1.773e-03  1.264e-02   0.140

Correlation of Fixed Effects:
            (Intr) PRED_T PRED_E
PRED_TRIPS  -0.353              
PRED_ENV    -0.586  0.109       
ECO_DIFFTRU -0.575  0.214 -0.221
convergence code: 0
boundary (singular) fit: see ?isSingular

Getting Model Coefficients from Summary: 
                  Estimate   Std. Error    t value
(Intercept)   8.944901e-01 1.455752e-02 61.4452131
PRED_TRIPS   -1.115435e-05 9.498993e-06 -1.1742663
PRED_ENV      2.317944e-02 4.706715e-03  4.9247586
ECO_DIFFTRUE  1.773206e-03 1.264349e-02  0.1402466

Getting Model ANOVA: 
Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + 
full_model:     (1 | DEST)
           Df     AIC     BIC logLik deviance  Chisq Chi Df Pr(>Chisq)
null_model  6 -226.56 -213.61 119.28  -238.56                         
full_model  7 -225.77 -210.66 119.89  -239.77 1.2075      1     0.2718

Plotting Model Coefficients: 
boundary (singular) fit: see ?isSingular

°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ 

Starting new analysis, with data index DIDX "8" and formula index FIDX "1" in Summary Tables.
Using formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 04_results_euk_otu99_deep_JAQU_model_data_2020-Jan-31-14-15-52_with_hon_info.csv. 

Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.
- Setting types.
- Input dimensions are: 69 17 .
- Removed variables are: ECO_PORT ECO_DEST VOY_FREQ B_FON_NOECO B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .
- Kept variables are: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST .
- Intermediate dimensions are: 69 6 .
- Undefined rows have been removed, assuming they were real "NA" and not "0".
- Final dimensions are: 69 6 .
# A tibble: 69 x 6
   RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT  DEST 
          <dbl>      <dbl>    <dbl> <fct>    <fct> <fct>
 1        0.955         10   1.45   TRUE     AD    BT   
 2        0.999          6   1.96   TRUE     AD    HT   
 3        0.984          2   2.15   TRUE     AD    WL   
 4        0.919         15   1.06   TRUE     AW    WL   
 5        0.848        189   1.56   TRUE     BT    AW   
 6        0.907         20   1.50   TRUE     BT    GH   
 7        0.973         11   2.92   TRUE     BT    HN   
 8        0.980        287   1.52   FALSE    BT    HT   
 9        0.938         26   2.14   TRUE     BT    LB   
10        0.934         75   3.16   FALSE    BT    MI   
11        0.971        221   1.56   FALSE    BT    NO   
12        0.888          6   1.50   TRUE     BT    OK   
13        0.982         17   1.41   TRUE     BT    PL   
14        0.904          5   1.49   TRUE     BT    RC   
15        0.928         83   1.57   TRUE     BT    RT   
16        0.935        180   0.921  FALSE    BT    WL   
17        0.932         94   2.25   TRUE     BT    ZB   
18        0.998         22   1.29   FALSE    CB    PL   
19        0.863         11   0.547  FALSE    CB    RC   
20        0.970          2   1.22   TRUE     CB    RT   
21        0.879         11   1.07   TRUE     GH    WL   
22        0.948         30   2.79   TRUE     HN    CB   
23        0.988         30   2.81   TRUE     HN    HT   
24        0.902          7   0.657  TRUE     HN    MI   
25        0.981        316   2.11   TRUE     HT    AW   
26        0.951         44   2.09   TRUE     HT    GH   
27        0.998         93   2.53   TRUE     HT    LB   
28        0.987        429   2.94   FALSE    HT    MI   
29        0.848       3937   0.0459 FALSE    HT    NO   
30        0.990          3   1.58   TRUE     HT    OK   
31        0.867         21   1.88   TRUE     HT    PL   
32        0.995          4   2.74   TRUE     HT    PM   
33        0.992         37   1.88   TRUE     HT    RC   
34        0.921        498   2.23   TRUE     HT    RT   
35        0.866         31   1.55   FALSE    HT    WL   
36        0.997         16   3.39   TRUE     HT    ZB   
37        0.902         74   1.94   FALSE    LB    CB   
38        0.908         11   1.50   TRUE     LB    MI   
39        0.991          3   2.92   TRUE     LB    WL   
40        0.967        114   4.15   TRUE     MI    AW   
41        0.990        185   2.94   FALSE    MI    NO   
42        0.928          8   3.38   TRUE     MI    OK   
43        0.977         44   4.18   TRUE     MI    RT   
44        0.966          2   3.49   TRUE     MI    ZB   
45        0.857         11   1.59   FALSE    NO    WL   
46        0.946          0   2.92   TRUE     PH    BT   
47        0.931          0   2.79   TRUE     PH    CB   
48        0.997          0   2.81   TRUE     PH    HT   
49        0.844          0   0.657  TRUE     PH    MI   
50        0.898          0   0.576  TRUE     PH    SI   
51        0.840          8   1.12   TRUE     RT    WL   
52        0.946        556   2.06   TRUE     SI    AD   
53        0.960        622   4.01   TRUE     SI    AW   
54        0.947        142   3.18   TRUE     SI    BT   
55        0.950         18   3.18   TRUE     SI    CB   
56        0.986         77   3.93   TRUE     SI    GH   
57        0.935        182   0.576  TRUE     SI    HN   
58        0.993        435   2.81   TRUE     SI    HT   
59        0.932        395   1.55   TRUE     SI    LB   
60        0.883         24   0.506  TRUE     SI    MI   
61        0.994        383   2.80   TRUE     SI    NO   
62        0.922        126   3.17   TRUE     SI    OK   
63        0.996        112   3.86   TRUE     SI    PL   
64        0.983         13   2.54   TRUE     SI    PM   
65        0.930         60   2.94   TRUE     SI    RC   
66        0.976       1055   4.05   TRUE     SI    RT   
67        0.986         12   3.84   TRUE     SI    WL   
68        0.952        207   3.51   TRUE     SI    ZB   
69        0.987          6   2.77   TRUE     WL    ZB   

Modelling function received variables: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 69 6 .
   ... formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Modelling function received variables: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 69 6 .
   ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Getting Model Summary: 
Linear mixed model fit by maximum likelihood  ['lmerMod']
Formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) +      (1 | DEST)
   Data: data_item

     AIC      BIC   logLik deviance df.resid 
  -245.5   -229.8    129.7   -259.5       62 

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-2.14268 -0.57961 -0.05054  0.80019  1.94776 

Random effects:
 Groups   Name        Variance  Std.Dev.
 DEST     (Intercept) 0.0001275 0.01129 
 PORT     (Intercept) 0.0000000 0.00000 
 Residual             0.0012530 0.03540 
Number of obs: 69, groups:  DEST, 18; PORT, 14

Fixed effects:
               Estimate Std. Error t value
(Intercept)   8.924e-01  1.437e-02  62.097
PRED_TRIPS   -1.142e-05  9.396e-06  -1.215
PRED_ENV      2.470e-02  4.420e-03   5.588
ECO_DIFFTRUE -9.740e-04  1.234e-02  -0.079

Correlation of Fixed Effects:
            (Intr) PRED_T PRED_E
PRED_TRIPS  -0.345              
PRED_ENV    -0.567  0.106       
ECO_DIFFTRU -0.616  0.221 -0.185
convergence code: 0
boundary (singular) fit: see ?isSingular

Getting Model Coefficients from Summary: 
                  Estimate   Std. Error     t value
(Intercept)   8.924022e-01 1.437121e-02 62.09653937
PRED_TRIPS   -1.141679e-05 9.395606e-06 -1.21512014
PRED_ENV      2.470216e-02 4.420316e-03  5.58832543
ECO_DIFFTRUE -9.739660e-04 1.233857e-02 -0.07893667

Getting Model ANOVA: 
Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + 
full_model:     (1 | DEST)
           Df     AIC     BIC logLik deviance  Chisq Chi Df Pr(>Chisq)
null_model  6 -246.15 -232.74 129.07  -258.15                         
full_model  7 -245.49 -229.85 129.74  -259.49 1.3392      1     0.2472

Plotting Model Coefficients: 
boundary (singular) fit: see ?isSingular

°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ 

Starting new analysis, with data index DIDX "9" and formula index FIDX "1" in Summary Tables.
Using formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 05_results_euk_asv00_shal_UNIF_model_data_2020-Jan-31-14-16-03_no_ph_with_hon_info.csv. 

Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.
- Setting types.
- Input dimensions are: 64 17 .
- Removed variables are: ECO_PORT ECO_DEST VOY_FREQ B_FON_NOECO B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .
- Kept variables are: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST .
- Intermediate dimensions are: 64 6 .
- Undefined rows have been removed, assuming they were real "NA" and not "0".
- Final dimensions are: 64 6 .
# A tibble: 64 x 6
   RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT  DEST 
          <dbl>      <dbl>    <dbl> <fct>    <fct> <fct>
 1        0.815         10   1.45   TRUE     AD    BT   
 2        0.925          6   1.96   TRUE     AD    HT   
 3        0.881          2   2.15   TRUE     AD    WL   
 4        0.702         15   1.06   TRUE     AW    WL   
 5        0.634        189   1.56   TRUE     BT    AW   
 6        0.713         20   1.50   TRUE     BT    GH   
 7        0.819         11   2.92   TRUE     BT    HN   
 8        0.800        287   1.52   FALSE    BT    HT   
 9        0.782         26   2.14   TRUE     BT    LB   
10        0.760         75   3.16   FALSE    BT    MI   
11        0.801        221   1.56   FALSE    BT    NO   
12        0.693          6   1.50   TRUE     BT    OK   
13        0.778         17   1.41   TRUE     BT    PL   
14        0.683          5   1.49   TRUE     BT    RC   
15        0.728         83   1.57   TRUE     BT    RT   
16        0.728        180   0.921  FALSE    BT    WL   
17        0.764         94   2.25   TRUE     BT    ZB   
18        0.836         22   1.29   FALSE    CB    PL   
19        0.683         11   0.547  FALSE    CB    RC   
20        0.779          2   1.22   TRUE     CB    RT   
21        0.673         11   1.07   TRUE     GH    WL   
22        0.741         30   2.79   TRUE     HN    CB   
23        0.840         30   2.81   TRUE     HN    HT   
24        0.736          7   0.657  TRUE     HN    MI   
25        0.779        316   2.11   TRUE     HT    AW   
26        0.765         44   2.09   TRUE     HT    GH   
27        0.889         93   2.53   TRUE     HT    LB   
28        0.849        429   2.94   FALSE    HT    MI   
29        0.632       3937   0.0459 FALSE    HT    NO   
30        0.829          3   1.58   TRUE     HT    OK   
31        0.645         21   1.88   TRUE     HT    PL   
32        0.838          4   2.74   TRUE     HT    PM   
33        0.839         37   1.88   TRUE     HT    RC   
34        0.701        498   2.23   TRUE     HT    RT   
35        0.653         31   1.55   FALSE    HT    WL   
36        0.874         16   3.39   TRUE     HT    ZB   
37        0.735         74   1.94   FALSE    LB    CB   
38        0.721         11   1.50   TRUE     LB    MI   
39        0.846          3   2.92   TRUE     LB    WL   
40        0.745        114   4.15   TRUE     MI    AW   
41        0.869        185   2.94   FALSE    MI    NO   
42        0.719          8   3.38   TRUE     MI    OK   
43        0.785         44   4.18   TRUE     MI    RT   
44        0.790          2   3.49   TRUE     MI    ZB   
45        0.658         11   1.59   FALSE    NO    WL   
46        0.603          8   1.12   TRUE     RT    WL   
47        0.796        556   2.06   TRUE     SI    AD   
48        0.741        622   4.01   TRUE     SI    AW   
49        0.743        142   3.18   TRUE     SI    BT   
50        0.737         18   3.18   TRUE     SI    CB   
51        0.797         77   3.93   TRUE     SI    GH   
52        0.761        182   0.576  TRUE     SI    HN   
53        0.842        435   2.81   TRUE     SI    HT   
54        0.727        395   1.55   TRUE     SI    LB   
55        0.692         24   0.506  TRUE     SI    MI   
56        0.858        383   2.80   TRUE     SI    NO   
57        0.706        126   3.17   TRUE     SI    OK   
58        0.841        112   3.86   TRUE     SI    PL   
59        0.781         13   2.54   TRUE     SI    PM   
60        0.699         60   2.94   TRUE     SI    RC   
61        0.777       1055   4.05   TRUE     SI    RT   
62        0.803         12   3.84   TRUE     SI    WL   
63        0.777        207   3.51   TRUE     SI    ZB   
64        0.813          6   2.77   TRUE     WL    ZB   

Modelling function received variables: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 64 6 .
   ... formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .

Modelling function received variables: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 64 6 .
   ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .

Getting Model Summary: 
Linear mixed model fit by maximum likelihood  ['lmerMod']
Formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) +      (1 | DEST)
   Data: data_item

     AIC      BIC   logLik deviance df.resid 
  -161.4   -146.2     87.7   -175.4       57 

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-2.73057 -0.51951  0.01372  0.66037  1.59054 

Random effects:
 Groups   Name        Variance  Std.Dev.
 DEST     (Intercept) 0.0009642 0.03105 
 PORT     (Intercept) 0.0011367 0.03371 
 Residual             0.0025268 0.05027 
Number of obs: 64, groups:  DEST, 17; PORT, 13

Fixed effects:
               Estimate Std. Error t value
(Intercept)   7.043e-01  2.853e-02  24.682
PRED_TRIPS   -3.207e-05  1.518e-05  -2.113
PRED_ENV      3.265e-02  7.982e-03   4.090
ECO_DIFFTRUE -3.711e-03  2.071e-02  -0.179

Correlation of Fixed Effects:
            (Intr) PRED_T PRED_E
PRED_TRIPS  -0.341              
PRED_ENV    -0.573  0.212       
ECO_DIFFTRU -0.595  0.247 -0.039

Getting Model Coefficients from Summary: 
                  Estimate   Std. Error    t value
(Intercept)   7.042866e-01 2.853415e-02 24.6822345
PRED_TRIPS   -3.207035e-05 1.517845e-05 -2.1128872
PRED_ENV      3.264965e-02 7.982089e-03  4.0903635
ECO_DIFFTRUE -3.711237e-03 2.070999e-02 -0.1792004

Getting Model ANOVA: 
Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + 
full_model:     (1 | DEST)
           Df     AIC     BIC logLik deviance  Chisq Chi Df Pr(>Chisq)  
null_model  6 -159.56 -146.60 85.778  -171.56                           
full_model  7 -161.36 -146.25 87.679  -175.36 3.8022      1    0.05119 .
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Plotting Model Coefficients: 

°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ 

Starting new analysis, with data index DIDX "10" and formula index FIDX "1" in Summary Tables.
Using formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 05_results_euk_asv00_shal_UNIF_model_data_2020-Jan-31-14-16-03_with_hon_info.csv. 

Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.
- Setting types.
- Input dimensions are: 69 17 .
- Removed variables are: ECO_PORT ECO_DEST VOY_FREQ B_FON_NOECO B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .
- Kept variables are: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST .
- Intermediate dimensions are: 69 6 .
- Undefined rows have been removed, assuming they were real "NA" and not "0".
- Final dimensions are: 69 6 .
# A tibble: 69 x 6
   RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT  DEST 
          <dbl>      <dbl>    <dbl> <fct>    <fct> <fct>
 1        0.815         10   1.45   TRUE     AD    BT   
 2        0.925          6   1.96   TRUE     AD    HT   
 3        0.881          2   2.15   TRUE     AD    WL   
 4        0.702         15   1.06   TRUE     AW    WL   
 5        0.634        189   1.56   TRUE     BT    AW   
 6        0.713         20   1.50   TRUE     BT    GH   
 7        0.819         11   2.92   TRUE     BT    HN   
 8        0.800        287   1.52   FALSE    BT    HT   
 9        0.782         26   2.14   TRUE     BT    LB   
10        0.760         75   3.16   FALSE    BT    MI   
11        0.801        221   1.56   FALSE    BT    NO   
12        0.693          6   1.50   TRUE     BT    OK   
13        0.778         17   1.41   TRUE     BT    PL   
14        0.683          5   1.49   TRUE     BT    RC   
15        0.728         83   1.57   TRUE     BT    RT   
16        0.728        180   0.921  FALSE    BT    WL   
17        0.764         94   2.25   TRUE     BT    ZB   
18        0.836         22   1.29   FALSE    CB    PL   
19        0.683         11   0.547  FALSE    CB    RC   
20        0.779          2   1.22   TRUE     CB    RT   
21        0.673         11   1.07   TRUE     GH    WL   
22        0.741         30   2.79   TRUE     HN    CB   
23        0.840         30   2.81   TRUE     HN    HT   
24        0.736          7   0.657  TRUE     HN    MI   
25        0.779        316   2.11   TRUE     HT    AW   
26        0.765         44   2.09   TRUE     HT    GH   
27        0.889         93   2.53   TRUE     HT    LB   
28        0.849        429   2.94   FALSE    HT    MI   
29        0.632       3937   0.0459 FALSE    HT    NO   
30        0.829          3   1.58   TRUE     HT    OK   
31        0.645         21   1.88   TRUE     HT    PL   
32        0.838          4   2.74   TRUE     HT    PM   
33        0.839         37   1.88   TRUE     HT    RC   
34        0.701        498   2.23   TRUE     HT    RT   
35        0.653         31   1.55   FALSE    HT    WL   
36        0.874         16   3.39   TRUE     HT    ZB   
37        0.735         74   1.94   FALSE    LB    CB   
38        0.721         11   1.50   TRUE     LB    MI   
39        0.846          3   2.92   TRUE     LB    WL   
40        0.745        114   4.15   TRUE     MI    AW   
41        0.869        185   2.94   FALSE    MI    NO   
42        0.719          8   3.38   TRUE     MI    OK   
43        0.785         44   4.18   TRUE     MI    RT   
44        0.790          2   3.49   TRUE     MI    ZB   
45        0.658         11   1.59   FALSE    NO    WL   
46        0.779          0   2.92   TRUE     PH    BT   
47        0.708          0   2.79   TRUE     PH    CB   
48        0.872          0   2.81   TRUE     PH    HT   
49        0.657          0   0.657  TRUE     PH    MI   
50        0.701          0   0.576  TRUE     PH    SI   
51        0.603          8   1.12   TRUE     RT    WL   
52        0.796        556   2.06   TRUE     SI    AD   
53        0.741        622   4.01   TRUE     SI    AW   
54        0.743        142   3.18   TRUE     SI    BT   
55        0.737         18   3.18   TRUE     SI    CB   
56        0.797         77   3.93   TRUE     SI    GH   
57        0.761        182   0.576  TRUE     SI    HN   
58        0.842        435   2.81   TRUE     SI    HT   
59        0.727        395   1.55   TRUE     SI    LB   
60        0.692         24   0.506  TRUE     SI    MI   
61        0.858        383   2.80   TRUE     SI    NO   
62        0.706        126   3.17   TRUE     SI    OK   
63        0.841        112   3.86   TRUE     SI    PL   
64        0.781         13   2.54   TRUE     SI    PM   
65        0.699         60   2.94   TRUE     SI    RC   
66        0.777       1055   4.05   TRUE     SI    RT   
67        0.803         12   3.84   TRUE     SI    WL   
68        0.777        207   3.51   TRUE     SI    ZB   
69        0.813          6   2.77   TRUE     WL    ZB   

Modelling function received variables: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 69 6 .
   ... formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model failed to converge with max|grad| = 0.0072036 (tol = 0.002, component 1)

Modelling function received variables: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 69 6 .
   ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .

Getting Model Summary: 
Linear mixed model fit by maximum likelihood  ['lmerMod']
Formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) +      (1 | DEST)
   Data: data_item

     AIC      BIC   logLik deviance df.resid 
  -177.8   -162.2     95.9   -191.8       62 

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-2.84056 -0.49506 -0.01335  0.63641  1.68332 

Random effects:
 Groups   Name        Variance Std.Dev.
 DEST     (Intercept) 0.001126 0.03355 
 PORT     (Intercept) 0.001092 0.03304 
 Residual             0.002338 0.04836 
Number of obs: 69, groups:  DEST, 18; PORT, 14

Fixed effects:
               Estimate Std. Error t value
(Intercept)   7.006e-01  2.743e-02  25.538
PRED_TRIPS   -3.332e-05  1.472e-05  -2.263
PRED_ENV      3.438e-02  7.387e-03   4.654
ECO_DIFFTRUE -5.084e-03  2.001e-02  -0.254

Correlation of Fixed Effects:
            (Intr) PRED_T PRED_E
PRED_TRIPS  -0.331              
PRED_ENV    -0.548  0.205       
ECO_DIFFTRU -0.625  0.246 -0.025
convergence code: 0
Model failed to converge with max|grad| = 0.0072036 (tol = 0.002, component 1)

Getting Model Coefficients from Summary: 
                  Estimate   Std. Error    t value
(Intercept)   7.005728e-01 2.743285e-02 25.5377325
PRED_TRIPS   -3.331905e-05 1.472425e-05 -2.2628692
PRED_ENV      3.438061e-02 7.387069e-03  4.6541618
ECO_DIFFTRUE -5.084094e-03 2.001283e-02 -0.2540418

Getting Model ANOVA: 
Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + 
full_model:     (1 | DEST)
           Df     AIC     BIC logLik deviance  Chisq Chi Df Pr(>Chisq)  
null_model  6 -175.38 -161.97 93.687  -187.38                           
full_model  7 -177.80 -162.16 95.899  -191.80 4.4231      1    0.03546 *
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Plotting Model Coefficients: 
Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model failed to converge with max|grad| = 0.0072036 (tol = 0.002, component 1)

°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ 

Starting new analysis, with data index DIDX "11" and formula index FIDX "1" in Summary Tables.
Using formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 06_results_euk_otu99_shal_UNIF_model_data_2020-Jan-31-14-16-14_no_ph_with_hon_info.csv. 

Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.
- Setting types.
- Input dimensions are: 64 17 .
- Removed variables are: ECO_PORT ECO_DEST VOY_FREQ B_FON_NOECO B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .
- Kept variables are: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST .
- Intermediate dimensions are: 64 6 .
- Undefined rows have been removed, assuming they were real "NA" and not "0".
- Final dimensions are: 64 6 .
# A tibble: 64 x 6
   RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT  DEST 
          <dbl>      <dbl>    <dbl> <fct>    <fct> <fct>
 1        0.826         10   1.45   TRUE     AD    BT   
 2        0.927          6   1.96   TRUE     AD    HT   
 3        0.884          2   2.15   TRUE     AD    WL   
 4        0.716         15   1.06   TRUE     AW    WL   
 5        0.625        189   1.56   TRUE     BT    AW   
 6        0.705         20   1.50   TRUE     BT    GH   
 7        0.811         11   2.92   TRUE     BT    HN   
 8        0.800        287   1.52   FALSE    BT    HT   
 9        0.777         26   2.14   TRUE     BT    LB   
10        0.750         75   3.16   FALSE    BT    MI   
11        0.795        221   1.56   FALSE    BT    NO   
12        0.695          6   1.50   TRUE     BT    OK   
13        0.783         17   1.41   TRUE     BT    PL   
14        0.678          5   1.49   TRUE     BT    RC   
15        0.735         83   1.57   TRUE     BT    RT   
16        0.733        180   0.921  FALSE    BT    WL   
17        0.776         94   2.25   TRUE     BT    ZB   
18        0.840         22   1.29   FALSE    CB    PL   
19        0.675         11   0.547  FALSE    CB    RC   
20        0.780          2   1.22   TRUE     CB    RT   
21        0.675         11   1.07   TRUE     GH    WL   
22        0.731         30   2.79   TRUE     HN    CB   
23        0.836         30   2.81   TRUE     HN    HT   
24        0.729          7   0.657  TRUE     HN    MI   
25        0.781        316   2.11   TRUE     HT    AW   
26        0.757         44   2.09   TRUE     HT    GH   
27        0.891         93   2.53   TRUE     HT    LB   
28        0.852        429   2.94   FALSE    HT    MI   
29        0.619       3937   0.0459 FALSE    HT    NO   
30        0.836          3   1.58   TRUE     HT    OK   
31        0.636         21   1.88   TRUE     HT    PL   
32        0.830          4   2.74   TRUE     HT    PM   
33        0.839         37   1.88   TRUE     HT    RC   
34        0.694        498   2.23   TRUE     HT    RT   
35        0.642         31   1.55   FALSE    HT    WL   
36        0.878         16   3.39   TRUE     HT    ZB   
37        0.724         74   1.94   FALSE    LB    CB   
38        0.710         11   1.50   TRUE     LB    MI   
39        0.847          3   2.92   TRUE     LB    WL   
40        0.745        114   4.15   TRUE     MI    AW   
41        0.870        185   2.94   FALSE    MI    NO   
42        0.706          8   3.38   TRUE     MI    OK   
43        0.793         44   4.18   TRUE     MI    RT   
44        0.801          2   3.49   TRUE     MI    ZB   
45        0.645         11   1.59   FALSE    NO    WL   
46        0.607          8   1.12   TRUE     RT    WL   
47        0.805        556   2.06   TRUE     SI    AD   
48        0.734        622   4.01   TRUE     SI    AW   
49        0.741        142   3.18   TRUE     SI    BT   
50        0.736         18   3.18   TRUE     SI    CB   
51        0.800         77   3.93   TRUE     SI    GH   
52        0.765        182   0.576  TRUE     SI    HN   
53        0.843        435   2.81   TRUE     SI    HT   
54        0.721        395   1.55   TRUE     SI    LB   
55        0.699         24   0.506  TRUE     SI    MI   
56        0.857        383   2.80   TRUE     SI    NO   
57        0.706        126   3.17   TRUE     SI    OK   
58        0.843        112   3.86   TRUE     SI    PL   
59        0.784         13   2.54   TRUE     SI    PM   
60        0.692         60   2.94   TRUE     SI    RC   
61        0.777       1055   4.05   TRUE     SI    RT   
62        0.806         12   3.84   TRUE     SI    WL   
63        0.777        207   3.51   TRUE     SI    ZB   
64        0.830          6   2.77   TRUE     WL    ZB   

Modelling function received variables: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 64 6 .
   ... formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .

Modelling function received variables: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 64 6 .
   ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .

Getting Model Summary: 
Linear mixed model fit by maximum likelihood  ['lmerMod']
Formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) +      (1 | DEST)
   Data: data_item

     AIC      BIC   logLik deviance df.resid 
  -153.9   -138.8     84.0   -167.9       57 

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.6048 -0.5659  0.0599  0.6676  1.5485 

Random effects:
 Groups   Name        Variance  Std.Dev.
 DEST     (Intercept) 0.0008742 0.02957 
 PORT     (Intercept) 0.0009123 0.03020 
 Residual             0.0030711 0.05542 
Number of obs: 64, groups:  DEST, 17; PORT, 13

Fixed effects:
               Estimate Std. Error t value
(Intercept)   7.054e-01  2.950e-02  23.916
PRED_TRIPS   -3.267e-05  1.638e-05  -1.994
PRED_ENV      3.089e-02  8.526e-03   3.623
ECO_DIFFTRUE -1.012e-03  2.206e-02  -0.046

Correlation of Fixed Effects:
            (Intr) PRED_T PRED_E
PRED_TRIPS  -0.348              
PRED_ENV    -0.588  0.200       
ECO_DIFFTRU -0.594  0.245 -0.062

Getting Model Coefficients from Summary: 
                  Estimate   Std. Error     t value
(Intercept)   7.054095e-01 2.949584e-02 23.91556221
PRED_TRIPS   -3.266542e-05 1.637927e-05 -1.99431443
PRED_ENV      3.089044e-02 8.526501e-03  3.62287454
ECO_DIFFTRUE -1.011739e-03 2.206397e-02 -0.04585479

Getting Model ANOVA: 
Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + 
full_model:     (1 | DEST)
           Df     AIC     BIC logLik deviance  Chisq Chi Df Pr(>Chisq)  
null_model  6 -152.53 -139.58 82.266  -164.53                           
full_model  7 -153.91 -138.80 83.956  -167.91 3.3786      1    0.06605 .
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Plotting Model Coefficients: 

°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ 

Starting new analysis, with data index DIDX "12" and formula index FIDX "1" in Summary Tables.
Using formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 06_results_euk_otu99_shal_UNIF_model_data_2020-Jan-31-14-16-14_with_hon_info.csv. 

Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.
- Setting types.
- Input dimensions are: 69 17 .
- Removed variables are: ECO_PORT ECO_DEST VOY_FREQ B_FON_NOECO B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .
- Kept variables are: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST .
- Intermediate dimensions are: 69 6 .
- Undefined rows have been removed, assuming they were real "NA" and not "0".
- Final dimensions are: 69 6 .
# A tibble: 69 x 6
   RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT  DEST 
          <dbl>      <dbl>    <dbl> <fct>    <fct> <fct>
 1        0.826         10   1.45   TRUE     AD    BT   
 2        0.927          6   1.96   TRUE     AD    HT   
 3        0.884          2   2.15   TRUE     AD    WL   
 4        0.716         15   1.06   TRUE     AW    WL   
 5        0.625        189   1.56   TRUE     BT    AW   
 6        0.705         20   1.50   TRUE     BT    GH   
 7        0.811         11   2.92   TRUE     BT    HN   
 8        0.800        287   1.52   FALSE    BT    HT   
 9        0.777         26   2.14   TRUE     BT    LB   
10        0.750         75   3.16   FALSE    BT    MI   
11        0.795        221   1.56   FALSE    BT    NO   
12        0.695          6   1.50   TRUE     BT    OK   
13        0.783         17   1.41   TRUE     BT    PL   
14        0.678          5   1.49   TRUE     BT    RC   
15        0.735         83   1.57   TRUE     BT    RT   
16        0.733        180   0.921  FALSE    BT    WL   
17        0.776         94   2.25   TRUE     BT    ZB   
18        0.840         22   1.29   FALSE    CB    PL   
19        0.675         11   0.547  FALSE    CB    RC   
20        0.780          2   1.22   TRUE     CB    RT   
21        0.675         11   1.07   TRUE     GH    WL   
22        0.731         30   2.79   TRUE     HN    CB   
23        0.836         30   2.81   TRUE     HN    HT   
24        0.729          7   0.657  TRUE     HN    MI   
25        0.781        316   2.11   TRUE     HT    AW   
26        0.757         44   2.09   TRUE     HT    GH   
27        0.891         93   2.53   TRUE     HT    LB   
28        0.852        429   2.94   FALSE    HT    MI   
29        0.619       3937   0.0459 FALSE    HT    NO   
30        0.836          3   1.58   TRUE     HT    OK   
31        0.636         21   1.88   TRUE     HT    PL   
32        0.830          4   2.74   TRUE     HT    PM   
33        0.839         37   1.88   TRUE     HT    RC   
34        0.694        498   2.23   TRUE     HT    RT   
35        0.642         31   1.55   FALSE    HT    WL   
36        0.878         16   3.39   TRUE     HT    ZB   
37        0.724         74   1.94   FALSE    LB    CB   
38        0.710         11   1.50   TRUE     LB    MI   
39        0.847          3   2.92   TRUE     LB    WL   
40        0.745        114   4.15   TRUE     MI    AW   
41        0.870        185   2.94   FALSE    MI    NO   
42        0.706          8   3.38   TRUE     MI    OK   
43        0.793         44   4.18   TRUE     MI    RT   
44        0.801          2   3.49   TRUE     MI    ZB   
45        0.645         11   1.59   FALSE    NO    WL   
46        0.765          0   2.92   TRUE     PH    BT   
47        0.698          0   2.79   TRUE     PH    CB   
48        0.871          0   2.81   TRUE     PH    HT   
49        0.643          0   0.657  TRUE     PH    MI   
50        0.703          0   0.576  TRUE     PH    SI   
51        0.607          8   1.12   TRUE     RT    WL   
52        0.805        556   2.06   TRUE     SI    AD   
53        0.734        622   4.01   TRUE     SI    AW   
54        0.741        142   3.18   TRUE     SI    BT   
55        0.736         18   3.18   TRUE     SI    CB   
56        0.800         77   3.93   TRUE     SI    GH   
57        0.765        182   0.576  TRUE     SI    HN   
58        0.843        435   2.81   TRUE     SI    HT   
59        0.721        395   1.55   TRUE     SI    LB   
60        0.699         24   0.506  TRUE     SI    MI   
61        0.857        383   2.80   TRUE     SI    NO   
62        0.706        126   3.17   TRUE     SI    OK   
63        0.843        112   3.86   TRUE     SI    PL   
64        0.784         13   2.54   TRUE     SI    PM   
65        0.692         60   2.94   TRUE     SI    RC   
66        0.777       1055   4.05   TRUE     SI    RT   
67        0.806         12   3.84   TRUE     SI    WL   
68        0.777        207   3.51   TRUE     SI    ZB   
69        0.830          6   2.77   TRUE     WL    ZB   

Modelling function received variables: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 69 6 .
   ... formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .

Modelling function received variables: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 69 6 .
   ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .

Getting Model Summary: 
Linear mixed model fit by maximum likelihood  ['lmerMod']
Formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) +      (1 | DEST)
   Data: data_item

     AIC      BIC   logLik deviance df.resid 
  -169.2   -153.5     91.6   -183.2       62 

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-2.72407 -0.56034  0.04259  0.61408  1.65843 

Random effects:
 Groups   Name        Variance Std.Dev.
 DEST     (Intercept) 0.001087 0.03297 
 PORT     (Intercept) 0.000962 0.03102 
 Residual             0.002826 0.05316 
Number of obs: 69, groups:  DEST, 18; PORT, 14

Fixed effects:
               Estimate Std. Error t value
(Intercept)   7.011e-01  2.868e-02  24.444
PRED_TRIPS   -3.432e-05  1.593e-05  -2.154
PRED_ENV      3.281e-02  7.924e-03   4.140
ECO_DIFFTRUE -3.041e-03  2.140e-02  -0.142

Correlation of Fixed Effects:
            (Intr) PRED_T PRED_E
PRED_TRIPS  -0.337              
PRED_ENV    -0.561  0.197       
ECO_DIFFTRU -0.627  0.245 -0.040

Getting Model Coefficients from Summary: 
                  Estimate   Std. Error    t value
(Intercept)   7.011007e-01 2.868200e-02 24.4439243
PRED_TRIPS   -3.431843e-05 1.592986e-05 -2.1543462
PRED_ENV      3.280768e-02 7.924264e-03  4.1401548
ECO_DIFFTRUE -3.041145e-03 2.140271e-02 -0.1420916

Getting Model ANOVA: 
Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + 
full_model:     (1 | DEST)
           Df     AIC     BIC logLik deviance  Chisq Chi Df Pr(>Chisq)  
null_model  6 -167.19 -153.79 89.597  -179.19                           
full_model  7 -169.19 -153.55 91.594  -183.19 3.9944      1    0.04565 *
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Plotting Model Coefficients: 

°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ 

Starting new analysis, with data index DIDX "13" and formula index FIDX "1" in Summary Tables.
Using formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 07_results_euk_asv00_shal_JAQU_model_data_2020-Jan-31-14-16-25_no_ph_with_hon_info.csv. 

Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.
- Setting types.
- Input dimensions are: 64 17 .
- Removed variables are: ECO_PORT ECO_DEST VOY_FREQ B_FON_NOECO B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .
- Kept variables are: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST .
- Intermediate dimensions are: 64 6 .
- Undefined rows have been removed, assuming they were real "NA" and not "0".
- Final dimensions are: 64 6 .
# A tibble: 64 x 6
   RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT  DEST 
          <dbl>      <dbl>    <dbl> <fct>    <fct> <fct>
 1        0.983         10   1.45   TRUE     AD    BT   
 2        1              6   1.96   TRUE     AD    HT   
 3        0.996          2   2.15   TRUE     AD    WL   
 4        0.951         15   1.06   TRUE     AW    WL   
 5        0.902        189   1.56   TRUE     BT    AW   
 6        0.943         20   1.50   TRUE     BT    GH   
 7        0.985         11   2.92   TRUE     BT    HN   
 8        0.989        287   1.52   FALSE    BT    HT   
 9        0.964         26   2.14   TRUE     BT    LB   
10        0.974         75   3.16   FALSE    BT    MI   
11        0.985        221   1.56   FALSE    BT    NO   
12        0.936          6   1.50   TRUE     BT    OK   
13        0.991         17   1.41   TRUE     BT    PL   
14        0.941          5   1.49   TRUE     BT    RC   
15        0.955         83   1.57   TRUE     BT    RT   
16        0.957        180   0.921  FALSE    BT    WL   
17        0.956         94   2.25   TRUE     BT    ZB   
18        1             22   1.29   FALSE    CB    PL   
19        0.907         11   0.547  FALSE    CB    RC   
20        0.989          2   1.22   TRUE     CB    RT   
21        0.913         11   1.07   TRUE     GH    WL   
22        0.980         30   2.79   TRUE     HN    CB   
23        0.993         30   2.81   TRUE     HN    HT   
24        0.938          7   0.657  TRUE     HN    MI   
25        0.988        316   2.11   TRUE     HT    AW   
26        0.969         44   2.09   TRUE     HT    GH   
27        1.00          93   2.53   TRUE     HT    LB   
28        0.994        429   2.94   FALSE    HT    MI   
29        0.891       3937   0.0459 FALSE    HT    NO   
30        0.998          3   1.58   TRUE     HT    OK   
31        0.915         21   1.88   TRUE     HT    PL   
32        0.997          4   2.74   TRUE     HT    PM   
33        0.997         37   1.88   TRUE     HT    RC   
34        0.948        498   2.23   TRUE     HT    RT   
35        0.905         31   1.55   FALSE    HT    WL   
36        0.998         16   3.39   TRUE     HT    ZB   
37        0.939         74   1.94   FALSE    LB    CB   
38        0.942         11   1.50   TRUE     LB    MI   
39        0.997          3   2.92   TRUE     LB    WL   
40        0.988        114   4.15   TRUE     MI    AW   
41        0.998        185   2.94   FALSE    MI    NO   
42        0.962          8   3.38   TRUE     MI    OK   
43        0.991         44   4.18   TRUE     MI    RT   
44        0.988          2   3.49   TRUE     MI    ZB   
45        0.902         11   1.59   FALSE    NO    WL   
46        0.894          8   1.12   TRUE     RT    WL   
47        0.971        556   2.06   TRUE     SI    AD   
48        0.986        622   4.01   TRUE     SI    AW   
49        0.973        142   3.18   TRUE     SI    BT   
50        0.982         18   3.18   TRUE     SI    CB   
51        0.996         77   3.93   TRUE     SI    GH   
52        0.958        182   0.576  TRUE     SI    HN   
53        0.997        435   2.81   TRUE     SI    HT   
54        0.966        395   1.55   TRUE     SI    LB   
55        0.926         24   0.506  TRUE     SI    MI   
56        0.998        383   2.80   TRUE     SI    NO   
57        0.959        126   3.17   TRUE     SI    OK   
58        0.998        112   3.86   TRUE     SI    PL   
59        0.996         13   2.54   TRUE     SI    PM   
60        0.967         60   2.94   TRUE     SI    RC   
61        0.993       1055   4.05   TRUE     SI    RT   
62        0.997         12   3.84   TRUE     SI    WL   
63        0.986        207   3.51   TRUE     SI    ZB   
64        0.996          6   2.77   TRUE     WL    ZB   

Modelling function received variables: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 64 6 .
   ... formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Modelling function received variables: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 64 6 .
   ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Getting Model Summary: 
Linear mixed model fit by maximum likelihood  ['lmerMod']
Formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) +      (1 | DEST)
   Data: data_item

     AIC      BIC   logLik deviance df.resid 
  -278.6   -263.5    146.3   -292.6       57 

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-2.20197 -0.58910 -0.03792  0.75457  1.98232 

Random effects:
 Groups   Name        Variance  Std.Dev.
 DEST     (Intercept) 2.311e-05 0.004807
 PORT     (Intercept) 0.000e+00 0.000000
 Residual             5.840e-04 0.024165
Number of obs: 64, groups:  DEST, 17; PORT, 13

Fixed effects:
               Estimate Std. Error t value
(Intercept)   9.283e-01  9.592e-03  96.779
PRED_TRIPS   -8.624e-06  6.287e-06  -1.372
PRED_ENV      1.766e-02  3.124e-03   5.653
ECO_DIFFTRUE  2.875e-03  8.383e-03   0.343

Correlation of Fixed Effects:
            (Intr) PRED_T PRED_E
PRED_TRIPS  -0.354              
PRED_ENV    -0.586  0.106       
ECO_DIFFTRU -0.574  0.217 -0.227
convergence code: 0
boundary (singular) fit: see ?isSingular

Getting Model Coefficients from Summary: 
                  Estimate   Std. Error    t value
(Intercept)   9.282843e-01 9.591843e-03 96.7785153
PRED_TRIPS   -8.624270e-06 6.287087e-06 -1.3717433
PRED_ENV      1.765906e-02 3.123606e-03  5.6534209
ECO_DIFFTRUE  2.874895e-03 8.382687e-03  0.3429563

Getting Model ANOVA: 
Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + 
full_model:     (1 | DEST)
           Df     AIC     BIC logLik deviance  Chisq Chi Df Pr(>Chisq)
null_model  6 -278.90 -265.95 145.45  -290.90                         
full_model  7 -278.57 -263.45 146.28  -292.57 1.6621      1     0.1973

Plotting Model Coefficients: 
boundary (singular) fit: see ?isSingular

°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ 

Starting new analysis, with data index DIDX "14" and formula index FIDX "1" in Summary Tables.
Using formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 07_results_euk_asv00_shal_JAQU_model_data_2020-Jan-31-14-16-25_with_hon_info.csv. 

Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.
- Setting types.
- Input dimensions are: 69 17 .
- Removed variables are: ECO_PORT ECO_DEST VOY_FREQ B_FON_NOECO B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .
- Kept variables are: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST .
- Intermediate dimensions are: 69 6 .
- Undefined rows have been removed, assuming they were real "NA" and not "0".
- Final dimensions are: 69 6 .
# A tibble: 69 x 6
   RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT  DEST 
          <dbl>      <dbl>    <dbl> <fct>    <fct> <fct>
 1        0.983         10   1.45   TRUE     AD    BT   
 2        1              6   1.96   TRUE     AD    HT   
 3        0.996          2   2.15   TRUE     AD    WL   
 4        0.951         15   1.06   TRUE     AW    WL   
 5        0.902        189   1.56   TRUE     BT    AW   
 6        0.943         20   1.50   TRUE     BT    GH   
 7        0.985         11   2.92   TRUE     BT    HN   
 8        0.989        287   1.52   FALSE    BT    HT   
 9        0.964         26   2.14   TRUE     BT    LB   
10        0.974         75   3.16   FALSE    BT    MI   
11        0.985        221   1.56   FALSE    BT    NO   
12        0.936          6   1.50   TRUE     BT    OK   
13        0.991         17   1.41   TRUE     BT    PL   
14        0.941          5   1.49   TRUE     BT    RC   
15        0.955         83   1.57   TRUE     BT    RT   
16        0.957        180   0.921  FALSE    BT    WL   
17        0.956         94   2.25   TRUE     BT    ZB   
18        1             22   1.29   FALSE    CB    PL   
19        0.907         11   0.547  FALSE    CB    RC   
20        0.989          2   1.22   TRUE     CB    RT   
21        0.913         11   1.07   TRUE     GH    WL   
22        0.980         30   2.79   TRUE     HN    CB   
23        0.993         30   2.81   TRUE     HN    HT   
24        0.938          7   0.657  TRUE     HN    MI   
25        0.988        316   2.11   TRUE     HT    AW   
26        0.969         44   2.09   TRUE     HT    GH   
27        1.00          93   2.53   TRUE     HT    LB   
28        0.994        429   2.94   FALSE    HT    MI   
29        0.891       3937   0.0459 FALSE    HT    NO   
30        0.998          3   1.58   TRUE     HT    OK   
31        0.915         21   1.88   TRUE     HT    PL   
32        0.997          4   2.74   TRUE     HT    PM   
33        0.997         37   1.88   TRUE     HT    RC   
34        0.948        498   2.23   TRUE     HT    RT   
35        0.905         31   1.55   FALSE    HT    WL   
36        0.998         16   3.39   TRUE     HT    ZB   
37        0.939         74   1.94   FALSE    LB    CB   
38        0.942         11   1.50   TRUE     LB    MI   
39        0.997          3   2.92   TRUE     LB    WL   
40        0.988        114   4.15   TRUE     MI    AW   
41        0.998        185   2.94   FALSE    MI    NO   
42        0.962          8   3.38   TRUE     MI    OK   
43        0.991         44   4.18   TRUE     MI    RT   
44        0.988          2   3.49   TRUE     MI    ZB   
45        0.902         11   1.59   FALSE    NO    WL   
46        0.974          0   2.92   TRUE     PH    BT   
47        0.972          0   2.79   TRUE     PH    CB   
48        0.999          0   2.81   TRUE     PH    HT   
49        0.909          0   0.657  TRUE     PH    MI   
50        0.932          0   0.576  TRUE     PH    SI   
51        0.894          8   1.12   TRUE     RT    WL   
52        0.971        556   2.06   TRUE     SI    AD   
53        0.986        622   4.01   TRUE     SI    AW   
54        0.973        142   3.18   TRUE     SI    BT   
55        0.982         18   3.18   TRUE     SI    CB   
56        0.996         77   3.93   TRUE     SI    GH   
57        0.958        182   0.576  TRUE     SI    HN   
58        0.997        435   2.81   TRUE     SI    HT   
59        0.966        395   1.55   TRUE     SI    LB   
60        0.926         24   0.506  TRUE     SI    MI   
61        0.998        383   2.80   TRUE     SI    NO   
62        0.959        126   3.17   TRUE     SI    OK   
63        0.998        112   3.86   TRUE     SI    PL   
64        0.996         13   2.54   TRUE     SI    PM   
65        0.967         60   2.94   TRUE     SI    RC   
66        0.993       1055   4.05   TRUE     SI    RT   
67        0.997         12   3.84   TRUE     SI    WL   
68        0.986        207   3.51   TRUE     SI    ZB   
69        0.996          6   2.77   TRUE     WL    ZB   

Modelling function received variables: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 69 6 .
   ... formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Modelling function received variables: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 69 6 .
   ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Getting Model Summary: 
Linear mixed model fit by maximum likelihood  ['lmerMod']
Formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) +      (1 | DEST)
   Data: data_item

     AIC      BIC   logLik deviance df.resid 
  -304.1   -288.5    159.1   -318.1       62 

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-2.17103 -0.56904 -0.07609  0.74790  2.02501 

Random effects:
 Groups   Name        Variance  Std.Dev. 
 DEST     (Intercept) 3.154e-05 5.616e-03
 PORT     (Intercept) 6.547e-14 2.559e-07
 Residual             5.536e-04 2.353e-02
Number of obs: 69, groups:  DEST, 18; PORT, 14

Fixed effects:
               Estimate Std. Error t value
(Intercept)   9.268e-01  9.293e-03  99.734
PRED_TRIPS   -8.491e-06  6.147e-06  -1.381
PRED_ENV      1.861e-02  2.906e-03   6.405
ECO_DIFFTRUE  1.448e-03  8.109e-03   0.179

Correlation of Fixed Effects:
            (Intr) PRED_T PRED_E
PRED_TRIPS  -0.349              
PRED_ENV    -0.566  0.098       
ECO_DIFFTRU -0.615  0.229 -0.197
convergence code: 0
boundary (singular) fit: see ?isSingular

Getting Model Coefficients from Summary: 
                  Estimate   Std. Error    t value
(Intercept)   9.267885e-01 9.292588e-03 99.7341681
PRED_TRIPS   -8.491409e-06 6.147101e-06 -1.3813682
PRED_ENV      1.861110e-02 2.905683e-03  6.4050684
ECO_DIFFTRUE  1.448431e-03 8.109489e-03  0.1786094

Getting Model ANOVA: 
Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + 
full_model:     (1 | DEST)
           Df     AIC     BIC logLik deviance  Chisq Chi Df Pr(>Chisq)
null_model  6 -304.42 -291.01 158.21  -316.42                         
full_model  7 -304.14 -288.50 159.07  -318.14 1.7183      1     0.1899

Plotting Model Coefficients: 
boundary (singular) fit: see ?isSingular

°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ 

Starting new analysis, with data index DIDX "15" and formula index FIDX "1" in Summary Tables.
Using formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 08_results_euk_otu99_shal_JAQU_model_data_2020-Jan-31-14-16-36_no_ph_with_hon_info.csv. 

Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.
- Setting types.
- Input dimensions are: 64 17 .
- Removed variables are: ECO_PORT ECO_DEST VOY_FREQ B_FON_NOECO B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .
- Kept variables are: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST .
- Intermediate dimensions are: 64 6 .
- Undefined rows have been removed, assuming they were real "NA" and not "0".
- Final dimensions are: 64 6 .
# A tibble: 64 x 6
   RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT  DEST 
          <dbl>      <dbl>    <dbl> <fct>    <fct> <fct>
 1        0.955         10   1.45   TRUE     AD    BT   
 2        0.999          6   1.96   TRUE     AD    HT   
 3        0.984          2   2.15   TRUE     AD    WL   
 4        0.919         15   1.06   TRUE     AW    WL   
 5        0.847        189   1.56   TRUE     BT    AW   
 6        0.906         20   1.50   TRUE     BT    GH   
 7        0.972         11   2.92   TRUE     BT    HN   
 8        0.980        287   1.52   FALSE    BT    HT   
 9        0.936         26   2.14   TRUE     BT    LB   
10        0.933         75   3.16   FALSE    BT    MI   
11        0.971        221   1.56   FALSE    BT    NO   
12        0.886          6   1.50   TRUE     BT    OK   
13        0.981         17   1.41   TRUE     BT    PL   
14        0.903          5   1.49   TRUE     BT    RC   
15        0.927         83   1.57   TRUE     BT    RT   
16        0.934        180   0.921  FALSE    BT    WL   
17        0.931         94   2.25   TRUE     BT    ZB   
18        0.998         22   1.29   FALSE    CB    PL   
19        0.863         11   0.547  FALSE    CB    RC   
20        0.970          2   1.22   TRUE     CB    RT   
21        0.879         11   1.07   TRUE     GH    WL   
22        0.947         30   2.79   TRUE     HN    CB   
23        0.988         30   2.81   TRUE     HN    HT   
24        0.899          7   0.657  TRUE     HN    MI   
25        0.981        316   2.11   TRUE     HT    AW   
26        0.951         44   2.09   TRUE     HT    GH   
27        0.998         93   2.53   TRUE     HT    LB   
28        0.987        429   2.94   FALSE    HT    MI   
29        0.848       3937   0.0459 FALSE    HT    NO   
30        0.991          3   1.58   TRUE     HT    OK   
31        0.866         21   1.88   TRUE     HT    PL   
32        0.995          4   2.74   TRUE     HT    PM   
33        0.993         37   1.88   TRUE     HT    RC   
34        0.920        498   2.23   TRUE     HT    RT   
35        0.865         31   1.55   FALSE    HT    WL   
36        0.997         16   3.39   TRUE     HT    ZB   
37        0.901         74   1.94   FALSE    LB    CB   
38        0.907         11   1.50   TRUE     LB    MI   
39        0.991          3   2.92   TRUE     LB    WL   
40        0.966        114   4.15   TRUE     MI    AW   
41        0.990        185   2.94   FALSE    MI    NO   
42        0.927          8   3.38   TRUE     MI    OK   
43        0.976         44   4.18   TRUE     MI    RT   
44        0.965          2   3.49   TRUE     MI    ZB   
45        0.857         11   1.59   FALSE    NO    WL   
46        0.839          8   1.12   TRUE     RT    WL   
47        0.945        556   2.06   TRUE     SI    AD   
48        0.960        622   4.01   TRUE     SI    AW   
49        0.947        142   3.18   TRUE     SI    BT   
50        0.950         18   3.18   TRUE     SI    CB   
51        0.986         77   3.93   TRUE     SI    GH   
52        0.935        182   0.576  TRUE     SI    HN   
53        0.994        435   2.81   TRUE     SI    HT   
54        0.931        395   1.55   TRUE     SI    LB   
55        0.881         24   0.506  TRUE     SI    MI   
56        0.995        383   2.80   TRUE     SI    NO   
57        0.919        126   3.17   TRUE     SI    OK   
58        0.997        112   3.86   TRUE     SI    PL   
59        0.983         13   2.54   TRUE     SI    PM   
60        0.928         60   2.94   TRUE     SI    RC   
61        0.976       1055   4.05   TRUE     SI    RT   
62        0.988         12   3.84   TRUE     SI    WL   
63        0.952        207   3.51   TRUE     SI    ZB   
64        0.987          6   2.77   TRUE     WL    ZB   

Modelling function received variables: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 64 6 .
   ... formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Modelling function received variables: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 64 6 .
   ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Getting Model Summary: 
Linear mixed model fit by maximum likelihood  ['lmerMod']
Formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) +      (1 | DEST)
   Data: data_item

     AIC      BIC   logLik deviance df.resid 
  -224.4   -209.3    119.2   -238.4       57 

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-2.14414 -0.62543 -0.02483  0.75743  1.94434 

Random effects:
 Groups   Name        Variance  Std.Dev. 
 DEST     (Intercept) 7.293e-05 8.540e-03
 PORT     (Intercept) 6.941e-12 2.635e-06
 Residual             1.345e-03 3.668e-02
Number of obs: 64, groups:  DEST, 17; PORT, 13

Fixed effects:
               Estimate Std. Error t value
(Intercept)   8.937e-01  1.472e-02  60.729
PRED_TRIPS   -1.107e-05  9.603e-06  -1.153
PRED_ENV      2.341e-02  4.759e-03   4.920
ECO_DIFFTRUE  1.586e-03  1.278e-02   0.124

Correlation of Fixed Effects:
            (Intr) PRED_T PRED_E
PRED_TRIPS  -0.353              
PRED_ENV    -0.586  0.109       
ECO_DIFFTRU -0.575  0.214 -0.222
convergence code: 0
boundary (singular) fit: see ?isSingular

Getting Model Coefficients from Summary: 
                  Estimate   Std. Error    t value
(Intercept)   8.936726e-01 1.471568e-02 60.7292745
PRED_TRIPS   -1.107246e-05 9.603340e-06 -1.1529799
PRED_ENV      2.341112e-02 4.758759e-03  4.9195848
ECO_DIFFTRUE  1.585891e-03 1.278295e-02  0.1240629

Getting Model ANOVA: 
Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + 
full_model:     (1 | DEST)
           Df     AIC     BIC logLik deviance  Chisq Chi Df Pr(>Chisq)
null_model  6 -225.21 -212.26 118.61  -237.21                         
full_model  7 -224.37 -209.26 119.19  -238.37 1.1632      1     0.2808

Plotting Model Coefficients: 
boundary (singular) fit: see ?isSingular

°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ 

Starting new analysis, with data index DIDX "16" and formula index FIDX "1" in Summary Tables.
Using formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 08_results_euk_otu99_shal_JAQU_model_data_2020-Jan-31-14-16-36_with_hon_info.csv. 

Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.
- Setting types.
- Input dimensions are: 69 17 .
- Removed variables are: ECO_PORT ECO_DEST VOY_FREQ B_FON_NOECO B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .
- Kept variables are: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST .
- Intermediate dimensions are: 69 6 .
- Undefined rows have been removed, assuming they were real "NA" and not "0".
- Final dimensions are: 69 6 .
# A tibble: 69 x 6
   RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT  DEST 
          <dbl>      <dbl>    <dbl> <fct>    <fct> <fct>
 1        0.955         10   1.45   TRUE     AD    BT   
 2        0.999          6   1.96   TRUE     AD    HT   
 3        0.984          2   2.15   TRUE     AD    WL   
 4        0.919         15   1.06   TRUE     AW    WL   
 5        0.847        189   1.56   TRUE     BT    AW   
 6        0.906         20   1.50   TRUE     BT    GH   
 7        0.972         11   2.92   TRUE     BT    HN   
 8        0.980        287   1.52   FALSE    BT    HT   
 9        0.936         26   2.14   TRUE     BT    LB   
10        0.933         75   3.16   FALSE    BT    MI   
11        0.971        221   1.56   FALSE    BT    NO   
12        0.886          6   1.50   TRUE     BT    OK   
13        0.981         17   1.41   TRUE     BT    PL   
14        0.903          5   1.49   TRUE     BT    RC   
15        0.927         83   1.57   TRUE     BT    RT   
16        0.934        180   0.921  FALSE    BT    WL   
17        0.931         94   2.25   TRUE     BT    ZB   
18        0.998         22   1.29   FALSE    CB    PL   
19        0.863         11   0.547  FALSE    CB    RC   
20        0.970          2   1.22   TRUE     CB    RT   
21        0.879         11   1.07   TRUE     GH    WL   
22        0.947         30   2.79   TRUE     HN    CB   
23        0.988         30   2.81   TRUE     HN    HT   
24        0.899          7   0.657  TRUE     HN    MI   
25        0.981        316   2.11   TRUE     HT    AW   
26        0.951         44   2.09   TRUE     HT    GH   
27        0.998         93   2.53   TRUE     HT    LB   
28        0.987        429   2.94   FALSE    HT    MI   
29        0.848       3937   0.0459 FALSE    HT    NO   
30        0.991          3   1.58   TRUE     HT    OK   
31        0.866         21   1.88   TRUE     HT    PL   
32        0.995          4   2.74   TRUE     HT    PM   
33        0.993         37   1.88   TRUE     HT    RC   
34        0.920        498   2.23   TRUE     HT    RT   
35        0.865         31   1.55   FALSE    HT    WL   
36        0.997         16   3.39   TRUE     HT    ZB   
37        0.901         74   1.94   FALSE    LB    CB   
38        0.907         11   1.50   TRUE     LB    MI   
39        0.991          3   2.92   TRUE     LB    WL   
40        0.966        114   4.15   TRUE     MI    AW   
41        0.990        185   2.94   FALSE    MI    NO   
42        0.927          8   3.38   TRUE     MI    OK   
43        0.976         44   4.18   TRUE     MI    RT   
44        0.965          2   3.49   TRUE     MI    ZB   
45        0.857         11   1.59   FALSE    NO    WL   
46        0.945          0   2.92   TRUE     PH    BT   
47        0.930          0   2.79   TRUE     PH    CB   
48        0.998          0   2.81   TRUE     PH    HT   
49        0.843          0   0.657  TRUE     PH    MI   
50        0.898          0   0.576  TRUE     PH    SI   
51        0.839          8   1.12   TRUE     RT    WL   
52        0.945        556   2.06   TRUE     SI    AD   
53        0.960        622   4.01   TRUE     SI    AW   
54        0.947        142   3.18   TRUE     SI    BT   
55        0.950         18   3.18   TRUE     SI    CB   
56        0.986         77   3.93   TRUE     SI    GH   
57        0.935        182   0.576  TRUE     SI    HN   
58        0.994        435   2.81   TRUE     SI    HT   
59        0.931        395   1.55   TRUE     SI    LB   
60        0.881         24   0.506  TRUE     SI    MI   
61        0.995        383   2.80   TRUE     SI    NO   
62        0.919        126   3.17   TRUE     SI    OK   
63        0.997        112   3.86   TRUE     SI    PL   
64        0.983         13   2.54   TRUE     SI    PM   
65        0.928         60   2.94   TRUE     SI    RC   
66        0.976       1055   4.05   TRUE     SI    RT   
67        0.988         12   3.84   TRUE     SI    WL   
68        0.952        207   3.51   TRUE     SI    ZB   
69        0.987          6   2.77   TRUE     WL    ZB   

Modelling function received variables: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 69 6 .
   ... formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Modelling function received variables: RESP_UNIFRAC PRED_TRIPS PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 69 6 .
   ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Getting Model Summary: 
Linear mixed model fit by maximum likelihood  ['lmerMod']
Formula: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) +      (1 | DEST)
   Data: data_item

     AIC      BIC   logLik deviance df.resid 
  -244.0   -228.4    129.0   -258.0       62 

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-2.12619 -0.58445 -0.05513  0.80749  1.93569 

Random effects:
 Groups   Name        Variance  Std.Dev.
 DEST     (Intercept) 0.0001328 0.01152 
 PORT     (Intercept) 0.0000000 0.00000 
 Residual             0.0012785 0.03576 
Number of obs: 69, groups:  DEST, 18; PORT, 14

Fixed effects:
               Estimate Std. Error t value
(Intercept)   8.916e-01  1.453e-02  61.351
PRED_TRIPS   -1.141e-05  9.497e-06  -1.201
PRED_ENV      2.491e-02  4.467e-03   5.576
ECO_DIFFTRUE -1.124e-03  1.247e-02  -0.090

Correlation of Fixed Effects:
            (Intr) PRED_T PRED_E
PRED_TRIPS  -0.345              
PRED_ENV    -0.567  0.106       
ECO_DIFFTRU -0.616  0.221 -0.185
convergence code: 0
boundary (singular) fit: see ?isSingular

Getting Model Coefficients from Summary: 
                  Estimate   Std. Error     t value
(Intercept)   0.8916461761 1.453361e-02 61.35063380
PRED_TRIPS   -0.0000114094 9.497040e-06 -1.20136431
PRED_ENV      0.0249085037 4.467148e-03  5.57592996
ECO_DIFFTRUE -0.0011238911 1.246941e-02 -0.09013187

Getting Model ANOVA: 
Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + 
full_model:     (1 | DEST)
           Df     AIC     BIC logLik deviance  Chisq Chi Df Pr(>Chisq)
null_model  6 -244.68 -231.28 128.34  -256.68                         
full_model  7 -243.99 -228.36 129.00  -257.99 1.3103      1     0.2523

Plotting Model Coefficients: 
boundary (singular) fit: see ?isSingular

°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ 

Starting new analysis, with data index DIDX "1" and formula index FIDX "2" in Summary Tables.
Using formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 01_results_euk_asv00_deep_UNIF_model_data_2020-Jan-31-14-15-13_no_ph_with_hon_info.csv. 

Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.
- Setting types.
- Input dimensions are: 64 17 .
- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST B_FON_NOECO B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .
- Kept variables are: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST .
- Intermediate dimensions are: 64 6 .
- Undefined rows have been removed, assuming they were real "NA" and not "0".
- Final dimensions are: 48 6 .
# A tibble: 48 x 6
   RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT  DEST 
          <dbl>    <dbl>    <dbl> <fct>    <fct> <fct>
 1        0.700        1   1.06   TRUE     AW    WL   
 2        0.636      163   1.56   TRUE     BT    AW   
 3        0.695        5   1.50   TRUE     BT    GH   
 4        0.794      304   1.52   FALSE    BT    HT   
 5        0.785        1   2.14   TRUE     BT    LB   
 6        0.756       67   3.16   FALSE    BT    MI   
 7        0.794      275   1.56   FALSE    BT    NO   
 8        0.732       60   1.57   TRUE     BT    RT   
 9        0.723      186   0.921  FALSE    BT    WL   
10        0.776      126   2.25   TRUE     BT    ZB   
11        0.832       16   1.29   FALSE    CB    PL   
12        0.683        6   0.547  FALSE    CB    RC   
13        0.654        1   1.07   TRUE     GH    WL   
14        0.739       16   2.79   TRUE     HN    CB   
15        0.829        1   2.81   TRUE     HN    HT   
16        0.774      224   2.11   TRUE     HT    AW   
17        0.747        8   2.09   TRUE     HT    GH   
18        0.885        6   2.53   TRUE     HT    LB   
19        0.845      209   2.94   FALSE    HT    MI   
20        0.628     3684   0.0459 FALSE    HT    NO   
21        0.828        1   2.74   TRUE     HT    PM   
22        0.695      127   2.23   TRUE     HT    RT   
23        0.639       13   1.55   FALSE    HT    WL   
24        0.869        4   3.39   TRUE     HT    ZB   
25        0.738       27   1.94   FALSE    LB    CB   
26        0.726        1   1.50   TRUE     LB    MI   
27        0.748       52   4.15   TRUE     MI    AW   
28        0.864       92   2.94   FALSE    MI    NO   
29        0.712        1   3.38   TRUE     MI    OK   
30        0.786        1   4.18   TRUE     MI    RT   
31        0.799        1   3.49   TRUE     MI    ZB   
32        0.603        2   1.12   TRUE     RT    WL   
33        0.815      117   2.06   TRUE     SI    AD   
34        0.740       10   4.01   TRUE     SI    AW   
35        0.748        3   3.18   TRUE     SI    BT   
36        0.724        1   3.18   TRUE     SI    CB   
37        0.789        7   3.93   TRUE     SI    GH   
38        0.762       53   0.576  TRUE     SI    HN   
39        0.836       53   2.81   TRUE     SI    HT   
40        0.730       82   1.55   TRUE     SI    LB   
41        0.853       65   2.80   TRUE     SI    NO   
42        0.692        3   3.17   TRUE     SI    OK   
43        0.835       14   3.86   TRUE     SI    PL   
44        0.780        5   2.54   TRUE     SI    PM   
45        0.691       19   2.94   TRUE     SI    RC   
46        0.774       37   4.05   TRUE     SI    RT   
47        0.789        2   3.51   TRUE     SI    ZB   
48        0.817        2   2.77   TRUE     WL    ZB   

Modelling function received variables: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .

Modelling function received variables: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Getting Model Summary: 
Linear mixed model fit by maximum likelihood  ['lmerMod']
Formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) +      (1 | DEST)
   Data: data_item

     AIC      BIC   logLik deviance df.resid 
  -139.2   -126.1     76.6   -153.2       41 

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-1.9285 -0.4822 -0.0895  0.5744  2.0118 

Random effects:
 Groups   Name        Variance  Std.Dev.
 DEST     (Intercept) 2.104e-03 0.045867
 PORT     (Intercept) 8.458e-05 0.009197
 Residual             1.290e-03 0.035917
Number of obs: 48, groups:  DEST, 17; PORT, 11

Fixed effects:
               Estimate Std. Error t value
(Intercept)   7.283e-01  2.397e-02  30.385
VOY_FREQ     -4.318e-05  1.279e-05  -3.377
PRED_ENV      2.514e-02  7.356e-03   3.418
ECO_DIFFTRUE -2.728e-02  1.677e-02  -1.627

Correlation of Fixed Effects:
            (Intr) VOY_FR PRED_E
VOY_FREQ    -0.391              
PRED_ENV    -0.635  0.377       
ECO_DIFFTRU -0.425  0.123 -0.172

Getting Model Coefficients from Summary: 
                  Estimate   Std. Error   t value
(Intercept)   7.282536e-01 2.396751e-02 30.385033
VOY_FREQ     -4.318023e-05 1.278701e-05 -3.376883
PRED_ENV      2.514240e-02 7.356143e-03  3.417878
ECO_DIFFTRUE -2.727844e-02 1.676922e-02 -1.626698

Getting Model ANOVA: 
Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + 
full_model:     (1 | DEST)
           Df     AIC     BIC logLik deviance  Chisq Chi Df Pr(>Chisq)   
null_model  6 -133.05 -121.82 72.525  -145.05                            
full_model  7 -139.19 -126.09 76.593  -153.19 8.1376      1   0.004336 **
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Plotting Model Coefficients: 

°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ 

Starting new analysis, with data index DIDX "2" and formula index FIDX "2" in Summary Tables.
Using formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 01_results_euk_asv00_deep_UNIF_model_data_2020-Jan-31-14-15-13_with_hon_info.csv. 

Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.
- Setting types.
- Input dimensions are: 69 17 .
- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST B_FON_NOECO B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .
- Kept variables are: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST .
- Intermediate dimensions are: 69 6 .
- Undefined rows have been removed, assuming they were real "NA" and not "0".
- Final dimensions are: 48 6 .
# A tibble: 48 x 6
   RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT  DEST 
          <dbl>    <dbl>    <dbl> <fct>    <fct> <fct>
 1        0.700        1   1.06   TRUE     AW    WL   
 2        0.636      163   1.56   TRUE     BT    AW   
 3        0.695        5   1.50   TRUE     BT    GH   
 4        0.794      304   1.52   FALSE    BT    HT   
 5        0.785        1   2.14   TRUE     BT    LB   
 6        0.756       67   3.16   FALSE    BT    MI   
 7        0.794      275   1.56   FALSE    BT    NO   
 8        0.732       60   1.57   TRUE     BT    RT   
 9        0.723      186   0.921  FALSE    BT    WL   
10        0.776      126   2.25   TRUE     BT    ZB   
11        0.832       16   1.29   FALSE    CB    PL   
12        0.683        6   0.547  FALSE    CB    RC   
13        0.654        1   1.07   TRUE     GH    WL   
14        0.739       16   2.79   TRUE     HN    CB   
15        0.829        1   2.81   TRUE     HN    HT   
16        0.774      224   2.11   TRUE     HT    AW   
17        0.747        8   2.09   TRUE     HT    GH   
18        0.885        6   2.53   TRUE     HT    LB   
19        0.845      209   2.94   FALSE    HT    MI   
20        0.628     3684   0.0459 FALSE    HT    NO   
21        0.828        1   2.74   TRUE     HT    PM   
22        0.695      127   2.23   TRUE     HT    RT   
23        0.639       13   1.55   FALSE    HT    WL   
24        0.869        4   3.39   TRUE     HT    ZB   
25        0.738       27   1.94   FALSE    LB    CB   
26        0.726        1   1.50   TRUE     LB    MI   
27        0.748       52   4.15   TRUE     MI    AW   
28        0.864       92   2.94   FALSE    MI    NO   
29        0.712        1   3.38   TRUE     MI    OK   
30        0.786        1   4.18   TRUE     MI    RT   
31        0.799        1   3.49   TRUE     MI    ZB   
32        0.603        2   1.12   TRUE     RT    WL   
33        0.815      117   2.06   TRUE     SI    AD   
34        0.740       10   4.01   TRUE     SI    AW   
35        0.748        3   3.18   TRUE     SI    BT   
36        0.724        1   3.18   TRUE     SI    CB   
37        0.789        7   3.93   TRUE     SI    GH   
38        0.762       53   0.576  TRUE     SI    HN   
39        0.836       53   2.81   TRUE     SI    HT   
40        0.730       82   1.55   TRUE     SI    LB   
41        0.853       65   2.80   TRUE     SI    NO   
42        0.692        3   3.17   TRUE     SI    OK   
43        0.835       14   3.86   TRUE     SI    PL   
44        0.780        5   2.54   TRUE     SI    PM   
45        0.691       19   2.94   TRUE     SI    RC   
46        0.774       37   4.05   TRUE     SI    RT   
47        0.789        2   3.51   TRUE     SI    ZB   
48        0.817        2   2.77   TRUE     WL    ZB   

Modelling function received variables: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .

Modelling function received variables: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Getting Model Summary: 
Linear mixed model fit by maximum likelihood  ['lmerMod']
Formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) +      (1 | DEST)
   Data: data_item

     AIC      BIC   logLik deviance df.resid 
  -139.2   -126.1     76.6   -153.2       41 

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-1.9285 -0.4822 -0.0895  0.5744  2.0118 

Random effects:
 Groups   Name        Variance  Std.Dev.
 DEST     (Intercept) 2.104e-03 0.045867
 PORT     (Intercept) 8.458e-05 0.009197
 Residual             1.290e-03 0.035917
Number of obs: 48, groups:  DEST, 17; PORT, 11

Fixed effects:
               Estimate Std. Error t value
(Intercept)   7.283e-01  2.397e-02  30.385
VOY_FREQ     -4.318e-05  1.279e-05  -3.377
PRED_ENV      2.514e-02  7.356e-03   3.418
ECO_DIFFTRUE -2.728e-02  1.677e-02  -1.627

Correlation of Fixed Effects:
            (Intr) VOY_FR PRED_E
VOY_FREQ    -0.391              
PRED_ENV    -0.635  0.377       
ECO_DIFFTRU -0.425  0.123 -0.172

Getting Model Coefficients from Summary: 
                  Estimate   Std. Error   t value
(Intercept)   7.282536e-01 2.396751e-02 30.385033
VOY_FREQ     -4.318023e-05 1.278701e-05 -3.376883
PRED_ENV      2.514240e-02 7.356143e-03  3.417878
ECO_DIFFTRUE -2.727844e-02 1.676922e-02 -1.626698

Getting Model ANOVA: 
Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + 
full_model:     (1 | DEST)
           Df     AIC     BIC logLik deviance  Chisq Chi Df Pr(>Chisq)   
null_model  6 -133.05 -121.82 72.525  -145.05                            
full_model  7 -139.19 -126.09 76.593  -153.19 8.1376      1   0.004336 **
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Plotting Model Coefficients: 

°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ 

Starting new analysis, with data index DIDX "3" and formula index FIDX "2" in Summary Tables.
Using formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 02_results_euk_otu99_deep_UNIF_model_data_2020-Jan-31-14-15-28_no_ph_with_hon_info.csv. 

Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.
- Setting types.
- Input dimensions are: 64 17 .
- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST B_FON_NOECO B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .
- Kept variables are: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST .
- Intermediate dimensions are: 64 6 .
- Undefined rows have been removed, assuming they were real "NA" and not "0".
- Final dimensions are: 48 6 .
# A tibble: 48 x 6
   RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT  DEST 
          <dbl>    <dbl>    <dbl> <fct>    <fct> <fct>
 1        0.708        1   1.06   TRUE     AW    WL   
 2        0.634      163   1.56   TRUE     BT    AW   
 3        0.708        5   1.50   TRUE     BT    GH   
 4        0.800      304   1.52   FALSE    BT    HT   
 5        0.791        1   2.14   TRUE     BT    LB   
 6        0.751       67   3.16   FALSE    BT    MI   
 7        0.802      275   1.56   FALSE    BT    NO   
 8        0.732       60   1.57   TRUE     BT    RT   
 9        0.740      186   0.921  FALSE    BT    WL   
10        0.786      126   2.25   TRUE     BT    ZB   
11        0.831       16   1.29   FALSE    CB    PL   
12        0.688        6   0.547  FALSE    CB    RC   
13        0.671        1   1.07   TRUE     GH    WL   
14        0.736       16   2.79   TRUE     HN    CB   
15        0.830        1   2.81   TRUE     HN    HT   
16        0.781      224   2.11   TRUE     HT    AW   
17        0.753        8   2.09   TRUE     HT    GH   
18        0.892        6   2.53   TRUE     HT    LB   
19        0.851      209   2.94   FALSE    HT    MI   
20        0.635     3684   0.0459 FALSE    HT    NO   
21        0.824        1   2.74   TRUE     HT    PM   
22        0.700      127   2.23   TRUE     HT    RT   
23        0.644       13   1.55   FALSE    HT    WL   
24        0.879        4   3.39   TRUE     HT    ZB   
25        0.730       27   1.94   FALSE    LB    CB   
26        0.726        1   1.50   TRUE     LB    MI   
27        0.747       52   4.15   TRUE     MI    AW   
28        0.870       92   2.94   FALSE    MI    NO   
29        0.715        1   3.38   TRUE     MI    OK   
30        0.789        1   4.18   TRUE     MI    RT   
31        0.802        1   3.49   TRUE     MI    ZB   
32        0.607        2   1.12   TRUE     RT    WL   
33        0.815      117   2.06   TRUE     SI    AD   
34        0.737       10   4.01   TRUE     SI    AW   
35        0.747        3   3.18   TRUE     SI    BT   
36        0.724        1   3.18   TRUE     SI    CB   
37        0.790        7   3.93   TRUE     SI    GH   
38        0.768       53   0.576  TRUE     SI    HN   
39        0.837       53   2.81   TRUE     SI    HT   
40        0.732       82   1.55   TRUE     SI    LB   
41        0.851       65   2.80   TRUE     SI    NO   
42        0.698        3   3.17   TRUE     SI    OK   
43        0.836       14   3.86   TRUE     SI    PL   
44        0.780        5   2.54   TRUE     SI    PM   
45        0.702       19   2.94   TRUE     SI    RC   
46        0.774       37   4.05   TRUE     SI    RT   
47        0.786        2   3.51   TRUE     SI    ZB   
48        0.821        2   2.77   TRUE     WL    ZB   

Modelling function received variables: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .

Modelling function received variables: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Getting Model Summary: 
Linear mixed model fit by maximum likelihood  ['lmerMod']
Formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) +      (1 | DEST)
   Data: data_item

     AIC      BIC   logLik deviance df.resid 
  -136.7   -123.6     75.3   -150.7       41 

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-1.94901 -0.45922 -0.01461  0.51781  2.03366 

Random effects:
 Groups   Name        Variance  Std.Dev.
 DEST     (Intercept) 1.950e-03 0.044156
 PORT     (Intercept) 7.704e-05 0.008777
 Residual             1.439e-03 0.037937
Number of obs: 48, groups:  DEST, 17; PORT, 11

Fixed effects:
               Estimate Std. Error t value
(Intercept)   7.332e-01  2.443e-02  30.008
VOY_FREQ     -4.176e-05  1.339e-05  -3.118
PRED_ENV      2.408e-02  7.616e-03   3.161
ECO_DIFFTRUE -2.718e-02  1.748e-02  -1.555

Correlation of Fixed Effects:
            (Intr) VOY_FR PRED_E
VOY_FREQ    -0.397              
PRED_ENV    -0.643  0.371       
ECO_DIFFTRU -0.427  0.125 -0.183

Getting Model Coefficients from Summary: 
                  Estimate   Std. Error   t value
(Intercept)   7.331661e-01 2.443236e-02 30.008001
VOY_FREQ     -4.175975e-05 1.339124e-05 -3.118439
PRED_ENV      2.407731e-02 7.616098e-03  3.161370
ECO_DIFFTRUE -2.717620e-02 1.748217e-02 -1.554509

Getting Model ANOVA: 
Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + 
full_model:     (1 | DEST)
           Df     AIC     BIC logLik deviance  Chisq Chi Df Pr(>Chisq)   
null_model  6 -131.56 -120.33 71.780  -143.56                            
full_model  7 -136.66 -123.56 75.332  -150.66 7.1038      1   0.007692 **
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Plotting Model Coefficients: 

°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ 

Starting new analysis, with data index DIDX "4" and formula index FIDX "2" in Summary Tables.
Using formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 02_results_euk_otu99_deep_UNIF_model_data_2020-Jan-31-14-15-28_with_hon_info.csv. 

Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.
- Setting types.
- Input dimensions are: 69 17 .
- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST B_FON_NOECO B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .
- Kept variables are: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST .
- Intermediate dimensions are: 69 6 .
- Undefined rows have been removed, assuming they were real "NA" and not "0".
- Final dimensions are: 48 6 .
# A tibble: 48 x 6
   RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT  DEST 
          <dbl>    <dbl>    <dbl> <fct>    <fct> <fct>
 1        0.708        1   1.06   TRUE     AW    WL   
 2        0.634      163   1.56   TRUE     BT    AW   
 3        0.708        5   1.50   TRUE     BT    GH   
 4        0.800      304   1.52   FALSE    BT    HT   
 5        0.791        1   2.14   TRUE     BT    LB   
 6        0.751       67   3.16   FALSE    BT    MI   
 7        0.802      275   1.56   FALSE    BT    NO   
 8        0.732       60   1.57   TRUE     BT    RT   
 9        0.740      186   0.921  FALSE    BT    WL   
10        0.786      126   2.25   TRUE     BT    ZB   
11        0.831       16   1.29   FALSE    CB    PL   
12        0.688        6   0.547  FALSE    CB    RC   
13        0.671        1   1.07   TRUE     GH    WL   
14        0.736       16   2.79   TRUE     HN    CB   
15        0.830        1   2.81   TRUE     HN    HT   
16        0.781      224   2.11   TRUE     HT    AW   
17        0.753        8   2.09   TRUE     HT    GH   
18        0.892        6   2.53   TRUE     HT    LB   
19        0.851      209   2.94   FALSE    HT    MI   
20        0.635     3684   0.0459 FALSE    HT    NO   
21        0.824        1   2.74   TRUE     HT    PM   
22        0.700      127   2.23   TRUE     HT    RT   
23        0.644       13   1.55   FALSE    HT    WL   
24        0.879        4   3.39   TRUE     HT    ZB   
25        0.730       27   1.94   FALSE    LB    CB   
26        0.726        1   1.50   TRUE     LB    MI   
27        0.747       52   4.15   TRUE     MI    AW   
28        0.870       92   2.94   FALSE    MI    NO   
29        0.715        1   3.38   TRUE     MI    OK   
30        0.789        1   4.18   TRUE     MI    RT   
31        0.802        1   3.49   TRUE     MI    ZB   
32        0.607        2   1.12   TRUE     RT    WL   
33        0.815      117   2.06   TRUE     SI    AD   
34        0.737       10   4.01   TRUE     SI    AW   
35        0.747        3   3.18   TRUE     SI    BT   
36        0.724        1   3.18   TRUE     SI    CB   
37        0.790        7   3.93   TRUE     SI    GH   
38        0.768       53   0.576  TRUE     SI    HN   
39        0.837       53   2.81   TRUE     SI    HT   
40        0.732       82   1.55   TRUE     SI    LB   
41        0.851       65   2.80   TRUE     SI    NO   
42        0.698        3   3.17   TRUE     SI    OK   
43        0.836       14   3.86   TRUE     SI    PL   
44        0.780        5   2.54   TRUE     SI    PM   
45        0.702       19   2.94   TRUE     SI    RC   
46        0.774       37   4.05   TRUE     SI    RT   
47        0.786        2   3.51   TRUE     SI    ZB   
48        0.821        2   2.77   TRUE     WL    ZB   

Modelling function received variables: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .

Modelling function received variables: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Getting Model Summary: 
Linear mixed model fit by maximum likelihood  ['lmerMod']
Formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) +      (1 | DEST)
   Data: data_item

     AIC      BIC   logLik deviance df.resid 
  -136.7   -123.6     75.3   -150.7       41 

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-1.94901 -0.45922 -0.01461  0.51781  2.03366 

Random effects:
 Groups   Name        Variance  Std.Dev.
 DEST     (Intercept) 1.950e-03 0.044156
 PORT     (Intercept) 7.704e-05 0.008777
 Residual             1.439e-03 0.037937
Number of obs: 48, groups:  DEST, 17; PORT, 11

Fixed effects:
               Estimate Std. Error t value
(Intercept)   7.332e-01  2.443e-02  30.008
VOY_FREQ     -4.176e-05  1.339e-05  -3.118
PRED_ENV      2.408e-02  7.616e-03   3.161
ECO_DIFFTRUE -2.718e-02  1.748e-02  -1.555

Correlation of Fixed Effects:
            (Intr) VOY_FR PRED_E
VOY_FREQ    -0.397              
PRED_ENV    -0.643  0.371       
ECO_DIFFTRU -0.427  0.125 -0.183

Getting Model Coefficients from Summary: 
                  Estimate   Std. Error   t value
(Intercept)   7.331661e-01 2.443236e-02 30.008001
VOY_FREQ     -4.175975e-05 1.339124e-05 -3.118439
PRED_ENV      2.407731e-02 7.616098e-03  3.161370
ECO_DIFFTRUE -2.717620e-02 1.748217e-02 -1.554509

Getting Model ANOVA: 
Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + 
full_model:     (1 | DEST)
           Df     AIC     BIC logLik deviance  Chisq Chi Df Pr(>Chisq)   
null_model  6 -131.56 -120.33 71.780  -143.56                            
full_model  7 -136.66 -123.56 75.332  -150.66 7.1038      1   0.007692 **
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Plotting Model Coefficients: 

°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ 

Starting new analysis, with data index DIDX "5" and formula index FIDX "2" in Summary Tables.
Using formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 03_results_euk_asv00_deep_JAQU_model_data_2020-Jan-31-14-15-41_no_ph_with_hon_info.csv. 

Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.
- Setting types.
- Input dimensions are: 64 17 .
- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST B_FON_NOECO B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .
- Kept variables are: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST .
- Intermediate dimensions are: 64 6 .
- Undefined rows have been removed, assuming they were real "NA" and not "0".
- Final dimensions are: 48 6 .
# A tibble: 48 x 6
   RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT  DEST 
          <dbl>    <dbl>    <dbl> <fct>    <fct> <fct>
 1        0.950        1   1.06   TRUE     AW    WL   
 2        0.902      163   1.56   TRUE     BT    AW   
 3        0.942        5   1.50   TRUE     BT    GH   
 4        0.989      304   1.52   FALSE    BT    HT   
 5        0.964        1   2.14   TRUE     BT    LB   
 6        0.974       67   3.16   FALSE    BT    MI   
 7        0.985      275   1.56   FALSE    BT    NO   
 8        0.955       60   1.57   TRUE     BT    RT   
 9        0.958      186   0.921  FALSE    BT    WL   
10        0.955      126   2.25   TRUE     BT    ZB   
11        1           16   1.29   FALSE    CB    PL   
12        0.908        6   0.547  FALSE    CB    RC   
13        0.913        1   1.07   TRUE     GH    WL   
14        0.980       16   2.79   TRUE     HN    CB   
15        0.993        1   2.81   TRUE     HN    HT   
16        0.988      224   2.11   TRUE     HT    AW   
17        0.969        8   2.09   TRUE     HT    GH   
18        1.00         6   2.53   TRUE     HT    LB   
19        0.994      209   2.94   FALSE    HT    MI   
20        0.892     3684   0.0459 FALSE    HT    NO   
21        0.997        1   2.74   TRUE     HT    PM   
22        0.948      127   2.23   TRUE     HT    RT   
23        0.906       13   1.55   FALSE    HT    WL   
24        0.999        4   3.39   TRUE     HT    ZB   
25        0.940       27   1.94   FALSE    LB    CB   
26        0.943        1   1.50   TRUE     LB    MI   
27        0.988       52   4.15   TRUE     MI    AW   
28        0.998       92   2.94   FALSE    MI    NO   
29        0.963        1   3.38   TRUE     MI    OK   
30        0.991        1   4.18   TRUE     MI    RT   
31        0.988        1   3.49   TRUE     MI    ZB   
32        0.894        2   1.12   TRUE     RT    WL   
33        0.971      117   2.06   TRUE     SI    AD   
34        0.985       10   4.01   TRUE     SI    AW   
35        0.973        3   3.18   TRUE     SI    BT   
36        0.981        1   3.18   TRUE     SI    CB   
37        0.995        7   3.93   TRUE     SI    GH   
38        0.959       53   0.576  TRUE     SI    HN   
39        0.997       53   2.81   TRUE     SI    HT   
40        0.967       82   1.55   TRUE     SI    LB   
41        0.997       65   2.80   TRUE     SI    NO   
42        0.958        3   3.17   TRUE     SI    OK   
43        0.998       14   3.86   TRUE     SI    PL   
44        0.996        5   2.54   TRUE     SI    PM   
45        0.965       19   2.94   TRUE     SI    RC   
46        0.992       37   4.05   TRUE     SI    RT   
47        0.984        2   3.51   TRUE     SI    ZB   
48        0.996        2   2.77   TRUE     WL    ZB   

Modelling function received variables: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Modelling function received variables: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Getting Model Summary: 
Linear mixed model fit by maximum likelihood  ['lmerMod']
Formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) +      (1 | DEST)
   Data: data_item

     AIC      BIC   logLik deviance df.resid 
  -220.0   -206.9    117.0   -234.0       41 

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-2.29190 -0.46783 -0.00733  0.61421  1.88346 

Random effects:
 Groups   Name        Variance  Std.Dev.
 DEST     (Intercept) 0.0001526 0.01235 
 PORT     (Intercept) 0.0000000 0.00000 
 Residual             0.0003375 0.01837 
Number of obs: 48, groups:  DEST, 17; PORT, 11

Fixed effects:
               Estimate Std. Error t value
(Intercept)   9.351e-01  9.819e-03  95.236
VOY_FREQ     -1.363e-05  6.112e-06  -2.230
PRED_ENV      1.770e-02  3.277e-03   5.403
ECO_DIFFTRUE -8.485e-03  7.770e-03  -1.092

Correlation of Fixed Effects:
            (Intr) VOY_FR PRED_E
VOY_FREQ    -0.431              
PRED_ENV    -0.667  0.336       
ECO_DIFFTRU -0.424  0.148 -0.253
convergence code: 0
boundary (singular) fit: see ?isSingular

Getting Model Coefficients from Summary: 
                  Estimate   Std. Error   t value
(Intercept)   9.350964e-01 9.818769e-03 95.235598
VOY_FREQ     -1.363075e-05 6.111967e-06 -2.230174
PRED_ENV      1.770408e-02 3.277001e-03  5.402524
ECO_DIFFTRUE -8.485289e-03 7.770235e-03 -1.092025

Getting Model ANOVA: 
Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + 
full_model:     (1 | DEST)
           Df     AIC     BIC logLik deviance  Chisq Chi Df Pr(>Chisq)  
null_model  6 -217.82 -206.59 114.91  -229.82                           
full_model  7 -220.05 -206.95 117.02  -234.05 4.2277      1    0.03977 *
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Plotting Model Coefficients: 
boundary (singular) fit: see ?isSingular

°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ 

Starting new analysis, with data index DIDX "6" and formula index FIDX "2" in Summary Tables.
Using formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 03_results_euk_asv00_deep_JAQU_model_data_2020-Jan-31-14-15-41_with_hon_info.csv. 

Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.
- Setting types.
- Input dimensions are: 69 17 .
- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST B_FON_NOECO B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .
- Kept variables are: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST .
- Intermediate dimensions are: 69 6 .
- Undefined rows have been removed, assuming they were real "NA" and not "0".
- Final dimensions are: 48 6 .
# A tibble: 48 x 6
   RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT  DEST 
          <dbl>    <dbl>    <dbl> <fct>    <fct> <fct>
 1        0.950        1   1.06   TRUE     AW    WL   
 2        0.902      163   1.56   TRUE     BT    AW   
 3        0.942        5   1.50   TRUE     BT    GH   
 4        0.989      304   1.52   FALSE    BT    HT   
 5        0.964        1   2.14   TRUE     BT    LB   
 6        0.974       67   3.16   FALSE    BT    MI   
 7        0.985      275   1.56   FALSE    BT    NO   
 8        0.955       60   1.57   TRUE     BT    RT   
 9        0.958      186   0.921  FALSE    BT    WL   
10        0.955      126   2.25   TRUE     BT    ZB   
11        1           16   1.29   FALSE    CB    PL   
12        0.908        6   0.547  FALSE    CB    RC   
13        0.913        1   1.07   TRUE     GH    WL   
14        0.980       16   2.79   TRUE     HN    CB   
15        0.993        1   2.81   TRUE     HN    HT   
16        0.988      224   2.11   TRUE     HT    AW   
17        0.969        8   2.09   TRUE     HT    GH   
18        1.00         6   2.53   TRUE     HT    LB   
19        0.994      209   2.94   FALSE    HT    MI   
20        0.892     3684   0.0459 FALSE    HT    NO   
21        0.997        1   2.74   TRUE     HT    PM   
22        0.948      127   2.23   TRUE     HT    RT   
23        0.906       13   1.55   FALSE    HT    WL   
24        0.999        4   3.39   TRUE     HT    ZB   
25        0.940       27   1.94   FALSE    LB    CB   
26        0.943        1   1.50   TRUE     LB    MI   
27        0.988       52   4.15   TRUE     MI    AW   
28        0.998       92   2.94   FALSE    MI    NO   
29        0.963        1   3.38   TRUE     MI    OK   
30        0.991        1   4.18   TRUE     MI    RT   
31        0.988        1   3.49   TRUE     MI    ZB   
32        0.894        2   1.12   TRUE     RT    WL   
33        0.971      117   2.06   TRUE     SI    AD   
34        0.985       10   4.01   TRUE     SI    AW   
35        0.973        3   3.18   TRUE     SI    BT   
36        0.981        1   3.18   TRUE     SI    CB   
37        0.995        7   3.93   TRUE     SI    GH   
38        0.959       53   0.576  TRUE     SI    HN   
39        0.997       53   2.81   TRUE     SI    HT   
40        0.967       82   1.55   TRUE     SI    LB   
41        0.997       65   2.80   TRUE     SI    NO   
42        0.958        3   3.17   TRUE     SI    OK   
43        0.998       14   3.86   TRUE     SI    PL   
44        0.996        5   2.54   TRUE     SI    PM   
45        0.965       19   2.94   TRUE     SI    RC   
46        0.992       37   4.05   TRUE     SI    RT   
47        0.984        2   3.51   TRUE     SI    ZB   
48        0.996        2   2.77   TRUE     WL    ZB   

Modelling function received variables: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Modelling function received variables: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Getting Model Summary: 
Linear mixed model fit by maximum likelihood  ['lmerMod']
Formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) +      (1 | DEST)
   Data: data_item

     AIC      BIC   logLik deviance df.resid 
  -220.0   -206.9    117.0   -234.0       41 

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-2.29190 -0.46783 -0.00733  0.61421  1.88346 

Random effects:
 Groups   Name        Variance  Std.Dev.
 DEST     (Intercept) 0.0001526 0.01235 
 PORT     (Intercept) 0.0000000 0.00000 
 Residual             0.0003375 0.01837 
Number of obs: 48, groups:  DEST, 17; PORT, 11

Fixed effects:
               Estimate Std. Error t value
(Intercept)   9.351e-01  9.819e-03  95.236
VOY_FREQ     -1.363e-05  6.112e-06  -2.230
PRED_ENV      1.770e-02  3.277e-03   5.403
ECO_DIFFTRUE -8.485e-03  7.770e-03  -1.092

Correlation of Fixed Effects:
            (Intr) VOY_FR PRED_E
VOY_FREQ    -0.431              
PRED_ENV    -0.667  0.336       
ECO_DIFFTRU -0.424  0.148 -0.253
convergence code: 0
boundary (singular) fit: see ?isSingular

Getting Model Coefficients from Summary: 
                  Estimate   Std. Error   t value
(Intercept)   9.350964e-01 9.818769e-03 95.235598
VOY_FREQ     -1.363075e-05 6.111967e-06 -2.230174
PRED_ENV      1.770408e-02 3.277001e-03  5.402524
ECO_DIFFTRUE -8.485289e-03 7.770235e-03 -1.092025

Getting Model ANOVA: 
Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + 
full_model:     (1 | DEST)
           Df     AIC     BIC logLik deviance  Chisq Chi Df Pr(>Chisq)  
null_model  6 -217.82 -206.59 114.91  -229.82                           
full_model  7 -220.05 -206.95 117.02  -234.05 4.2277      1    0.03977 *
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Plotting Model Coefficients: 
boundary (singular) fit: see ?isSingular

°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ 

Starting new analysis, with data index DIDX "7" and formula index FIDX "2" in Summary Tables.
Using formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 04_results_euk_otu99_deep_JAQU_model_data_2020-Jan-31-14-15-52_no_ph_with_hon_info.csv. 

Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.
- Setting types.
- Input dimensions are: 64 17 .
- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST B_FON_NOECO B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .
- Kept variables are: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST .
- Intermediate dimensions are: 64 6 .
- Undefined rows have been removed, assuming they were real "NA" and not "0".
- Final dimensions are: 48 6 .
# A tibble: 48 x 6
   RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT  DEST 
          <dbl>    <dbl>    <dbl> <fct>    <fct> <fct>
 1        0.919        1   1.06   TRUE     AW    WL   
 2        0.848      163   1.56   TRUE     BT    AW   
 3        0.907        5   1.50   TRUE     BT    GH   
 4        0.980      304   1.52   FALSE    BT    HT   
 5        0.938        1   2.14   TRUE     BT    LB   
 6        0.934       67   3.16   FALSE    BT    MI   
 7        0.971      275   1.56   FALSE    BT    NO   
 8        0.928       60   1.57   TRUE     BT    RT   
 9        0.935      186   0.921  FALSE    BT    WL   
10        0.932      126   2.25   TRUE     BT    ZB   
11        0.998       16   1.29   FALSE    CB    PL   
12        0.863        6   0.547  FALSE    CB    RC   
13        0.879        1   1.07   TRUE     GH    WL   
14        0.948       16   2.79   TRUE     HN    CB   
15        0.988        1   2.81   TRUE     HN    HT   
16        0.981      224   2.11   TRUE     HT    AW   
17        0.951        8   2.09   TRUE     HT    GH   
18        0.998        6   2.53   TRUE     HT    LB   
19        0.987      209   2.94   FALSE    HT    MI   
20        0.848     3684   0.0459 FALSE    HT    NO   
21        0.995        1   2.74   TRUE     HT    PM   
22        0.921      127   2.23   TRUE     HT    RT   
23        0.866       13   1.55   FALSE    HT    WL   
24        0.997        4   3.39   TRUE     HT    ZB   
25        0.902       27   1.94   FALSE    LB    CB   
26        0.908        1   1.50   TRUE     LB    MI   
27        0.967       52   4.15   TRUE     MI    AW   
28        0.990       92   2.94   FALSE    MI    NO   
29        0.928        1   3.38   TRUE     MI    OK   
30        0.977        1   4.18   TRUE     MI    RT   
31        0.966        1   3.49   TRUE     MI    ZB   
32        0.840        2   1.12   TRUE     RT    WL   
33        0.946      117   2.06   TRUE     SI    AD   
34        0.960       10   4.01   TRUE     SI    AW   
35        0.947        3   3.18   TRUE     SI    BT   
36        0.950        1   3.18   TRUE     SI    CB   
37        0.986        7   3.93   TRUE     SI    GH   
38        0.935       53   0.576  TRUE     SI    HN   
39        0.993       53   2.81   TRUE     SI    HT   
40        0.932       82   1.55   TRUE     SI    LB   
41        0.994       65   2.80   TRUE     SI    NO   
42        0.922        3   3.17   TRUE     SI    OK   
43        0.996       14   3.86   TRUE     SI    PL   
44        0.983        5   2.54   TRUE     SI    PM   
45        0.930       19   2.94   TRUE     SI    RC   
46        0.976       37   4.05   TRUE     SI    RT   
47        0.952        2   3.51   TRUE     SI    ZB   
48        0.987        2   2.77   TRUE     WL    ZB   

Modelling function received variables: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Modelling function received variables: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Getting Model Summary: 
Linear mixed model fit by maximum likelihood  ['lmerMod']
Formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) +      (1 | DEST)
   Data: data_item

     AIC      BIC   logLik deviance df.resid 
  -179.0   -165.9     96.5   -193.0       41 

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-2.30511 -0.46831 -0.09867  0.67532  2.08860 

Random effects:
 Groups   Name        Variance  Std.Dev. 
 DEST     (Intercept) 4.148e-04 2.037e-02
 PORT     (Intercept) 4.686e-12 2.165e-06
 Residual             7.653e-04 2.766e-02
Number of obs: 48, groups:  DEST, 17; PORT, 11

Fixed effects:
               Estimate Std. Error t value
(Intercept)   9.040e-01  1.507e-02  59.997
VOY_FREQ     -1.878e-05  9.269e-06  -2.027
PRED_ENV      2.280e-02  4.991e-03   4.568
ECO_DIFFTRUE -1.259e-02  1.181e-02  -1.066

Correlation of Fixed Effects:
            (Intr) VOY_FR PRED_E
VOY_FREQ    -0.426              
PRED_ENV    -0.663  0.341       
ECO_DIFFTRU -0.422  0.142 -0.251
convergence code: 0
boundary (singular) fit: see ?isSingular

Getting Model Coefficients from Summary: 
                  Estimate   Std. Error   t value
(Intercept)   0.9040493388 1.506822e-02 59.997088
VOY_FREQ     -0.0000187841 9.268835e-06 -2.026587
PRED_ENV      0.0228014076 4.991360e-03  4.568176
ECO_DIFFTRUE -0.0125892017 1.181367e-02 -1.065647

Getting Model ANOVA: 
Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + 
full_model:     (1 | DEST)
           Df     AIC     BIC logLik deviance  Chisq Chi Df Pr(>Chisq)  
null_model  6 -177.49 -166.26 94.743  -189.49                           
full_model  7 -179.05 -165.95 96.523  -193.05 3.5598      1    0.05919 .
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Plotting Model Coefficients: 
boundary (singular) fit: see ?isSingular

°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ 

Starting new analysis, with data index DIDX "8" and formula index FIDX "2" in Summary Tables.
Using formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 04_results_euk_otu99_deep_JAQU_model_data_2020-Jan-31-14-15-52_with_hon_info.csv. 

Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.
- Setting types.
- Input dimensions are: 69 17 .
- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST B_FON_NOECO B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .
- Kept variables are: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST .
- Intermediate dimensions are: 69 6 .
- Undefined rows have been removed, assuming they were real "NA" and not "0".
- Final dimensions are: 48 6 .
# A tibble: 48 x 6
   RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT  DEST 
          <dbl>    <dbl>    <dbl> <fct>    <fct> <fct>
 1        0.919        1   1.06   TRUE     AW    WL   
 2        0.848      163   1.56   TRUE     BT    AW   
 3        0.907        5   1.50   TRUE     BT    GH   
 4        0.980      304   1.52   FALSE    BT    HT   
 5        0.938        1   2.14   TRUE     BT    LB   
 6        0.934       67   3.16   FALSE    BT    MI   
 7        0.971      275   1.56   FALSE    BT    NO   
 8        0.928       60   1.57   TRUE     BT    RT   
 9        0.935      186   0.921  FALSE    BT    WL   
10        0.932      126   2.25   TRUE     BT    ZB   
11        0.998       16   1.29   FALSE    CB    PL   
12        0.863        6   0.547  FALSE    CB    RC   
13        0.879        1   1.07   TRUE     GH    WL   
14        0.948       16   2.79   TRUE     HN    CB   
15        0.988        1   2.81   TRUE     HN    HT   
16        0.981      224   2.11   TRUE     HT    AW   
17        0.951        8   2.09   TRUE     HT    GH   
18        0.998        6   2.53   TRUE     HT    LB   
19        0.987      209   2.94   FALSE    HT    MI   
20        0.848     3684   0.0459 FALSE    HT    NO   
21        0.995        1   2.74   TRUE     HT    PM   
22        0.921      127   2.23   TRUE     HT    RT   
23        0.866       13   1.55   FALSE    HT    WL   
24        0.997        4   3.39   TRUE     HT    ZB   
25        0.902       27   1.94   FALSE    LB    CB   
26        0.908        1   1.50   TRUE     LB    MI   
27        0.967       52   4.15   TRUE     MI    AW   
28        0.990       92   2.94   FALSE    MI    NO   
29        0.928        1   3.38   TRUE     MI    OK   
30        0.977        1   4.18   TRUE     MI    RT   
31        0.966        1   3.49   TRUE     MI    ZB   
32        0.840        2   1.12   TRUE     RT    WL   
33        0.946      117   2.06   TRUE     SI    AD   
34        0.960       10   4.01   TRUE     SI    AW   
35        0.947        3   3.18   TRUE     SI    BT   
36        0.950        1   3.18   TRUE     SI    CB   
37        0.986        7   3.93   TRUE     SI    GH   
38        0.935       53   0.576  TRUE     SI    HN   
39        0.993       53   2.81   TRUE     SI    HT   
40        0.932       82   1.55   TRUE     SI    LB   
41        0.994       65   2.80   TRUE     SI    NO   
42        0.922        3   3.17   TRUE     SI    OK   
43        0.996       14   3.86   TRUE     SI    PL   
44        0.983        5   2.54   TRUE     SI    PM   
45        0.930       19   2.94   TRUE     SI    RC   
46        0.976       37   4.05   TRUE     SI    RT   
47        0.952        2   3.51   TRUE     SI    ZB   
48        0.987        2   2.77   TRUE     WL    ZB   

Modelling function received variables: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Modelling function received variables: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Getting Model Summary: 
Linear mixed model fit by maximum likelihood  ['lmerMod']
Formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) +      (1 | DEST)
   Data: data_item

     AIC      BIC   logLik deviance df.resid 
  -179.0   -165.9     96.5   -193.0       41 

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-2.30511 -0.46831 -0.09867  0.67532  2.08860 

Random effects:
 Groups   Name        Variance  Std.Dev. 
 DEST     (Intercept) 4.148e-04 2.037e-02
 PORT     (Intercept) 4.686e-12 2.165e-06
 Residual             7.653e-04 2.766e-02
Number of obs: 48, groups:  DEST, 17; PORT, 11

Fixed effects:
               Estimate Std. Error t value
(Intercept)   9.040e-01  1.507e-02  59.997
VOY_FREQ     -1.878e-05  9.269e-06  -2.027
PRED_ENV      2.280e-02  4.991e-03   4.568
ECO_DIFFTRUE -1.259e-02  1.181e-02  -1.066

Correlation of Fixed Effects:
            (Intr) VOY_FR PRED_E
VOY_FREQ    -0.426              
PRED_ENV    -0.663  0.341       
ECO_DIFFTRU -0.422  0.142 -0.251
convergence code: 0
boundary (singular) fit: see ?isSingular

Getting Model Coefficients from Summary: 
                  Estimate   Std. Error   t value
(Intercept)   0.9040493388 1.506822e-02 59.997088
VOY_FREQ     -0.0000187841 9.268835e-06 -2.026587
PRED_ENV      0.0228014076 4.991360e-03  4.568176
ECO_DIFFTRUE -0.0125892017 1.181367e-02 -1.065647

Getting Model ANOVA: 
Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + 
full_model:     (1 | DEST)
           Df     AIC     BIC logLik deviance  Chisq Chi Df Pr(>Chisq)  
null_model  6 -177.49 -166.26 94.743  -189.49                           
full_model  7 -179.05 -165.95 96.523  -193.05 3.5598      1    0.05919 .
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Plotting Model Coefficients: 
boundary (singular) fit: see ?isSingular

°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ 

Starting new analysis, with data index DIDX "9" and formula index FIDX "2" in Summary Tables.
Using formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 05_results_euk_asv00_shal_UNIF_model_data_2020-Jan-31-14-16-03_no_ph_with_hon_info.csv. 

Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.
- Setting types.
- Input dimensions are: 64 17 .
- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST B_FON_NOECO B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .
- Kept variables are: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST .
- Intermediate dimensions are: 64 6 .
- Undefined rows have been removed, assuming they were real "NA" and not "0".
- Final dimensions are: 48 6 .
# A tibble: 48 x 6
   RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT  DEST 
          <dbl>    <dbl>    <dbl> <fct>    <fct> <fct>
 1        0.702        1   1.06   TRUE     AW    WL   
 2        0.634      163   1.56   TRUE     BT    AW   
 3        0.713        5   1.50   TRUE     BT    GH   
 4        0.800      304   1.52   FALSE    BT    HT   
 5        0.782        1   2.14   TRUE     BT    LB   
 6        0.760       67   3.16   FALSE    BT    MI   
 7        0.801      275   1.56   FALSE    BT    NO   
 8        0.728       60   1.57   TRUE     BT    RT   
 9        0.728      186   0.921  FALSE    BT    WL   
10        0.764      126   2.25   TRUE     BT    ZB   
11        0.836       16   1.29   FALSE    CB    PL   
12        0.683        6   0.547  FALSE    CB    RC   
13        0.673        1   1.07   TRUE     GH    WL   
14        0.741       16   2.79   TRUE     HN    CB   
15        0.840        1   2.81   TRUE     HN    HT   
16        0.779      224   2.11   TRUE     HT    AW   
17        0.765        8   2.09   TRUE     HT    GH   
18        0.889        6   2.53   TRUE     HT    LB   
19        0.849      209   2.94   FALSE    HT    MI   
20        0.632     3684   0.0459 FALSE    HT    NO   
21        0.838        1   2.74   TRUE     HT    PM   
22        0.701      127   2.23   TRUE     HT    RT   
23        0.653       13   1.55   FALSE    HT    WL   
24        0.874        4   3.39   TRUE     HT    ZB   
25        0.735       27   1.94   FALSE    LB    CB   
26        0.721        1   1.50   TRUE     LB    MI   
27        0.745       52   4.15   TRUE     MI    AW   
28        0.869       92   2.94   FALSE    MI    NO   
29        0.719        1   3.38   TRUE     MI    OK   
30        0.785        1   4.18   TRUE     MI    RT   
31        0.790        1   3.49   TRUE     MI    ZB   
32        0.603        2   1.12   TRUE     RT    WL   
33        0.796      117   2.06   TRUE     SI    AD   
34        0.741       10   4.01   TRUE     SI    AW   
35        0.743        3   3.18   TRUE     SI    BT   
36        0.737        1   3.18   TRUE     SI    CB   
37        0.797        7   3.93   TRUE     SI    GH   
38        0.761       53   0.576  TRUE     SI    HN   
39        0.842       53   2.81   TRUE     SI    HT   
40        0.727       82   1.55   TRUE     SI    LB   
41        0.858       65   2.80   TRUE     SI    NO   
42        0.706        3   3.17   TRUE     SI    OK   
43        0.841       14   3.86   TRUE     SI    PL   
44        0.781        5   2.54   TRUE     SI    PM   
45        0.699       19   2.94   TRUE     SI    RC   
46        0.777       37   4.05   TRUE     SI    RT   
47        0.777        2   3.51   TRUE     SI    ZB   
48        0.813        2   2.77   TRUE     WL    ZB   

Modelling function received variables: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .

Modelling function received variables: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .

Getting Model Summary: 
Linear mixed model fit by maximum likelihood  ['lmerMod']
Formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) +      (1 | DEST)
   Data: data_item

     AIC      BIC   logLik deviance df.resid 
  -139.5   -126.4     76.8   -153.5       41 

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-1.99915 -0.41205 -0.09242  0.58291  1.93039 

Random effects:
 Groups   Name        Variance  Std.Dev.
 DEST     (Intercept) 0.0019784 0.04448 
 PORT     (Intercept) 0.0001904 0.01380 
 Residual             0.0012387 0.03520 
Number of obs: 48, groups:  DEST, 17; PORT, 11

Fixed effects:
               Estimate Std. Error t value
(Intercept)   7.274e-01  2.474e-02  29.401
VOY_FREQ     -4.568e-05  1.275e-05  -3.581
PRED_ENV      2.649e-02  7.604e-03   3.484
ECO_DIFFTRUE -2.653e-02  1.693e-02  -1.567

Correlation of Fixed Effects:
            (Intr) VOY_FR PRED_E
VOY_FREQ    -0.393              
PRED_ENV    -0.648  0.387       
ECO_DIFFTRU -0.450  0.134 -0.120

Getting Model Coefficients from Summary: 
                  Estimate   Std. Error   t value
(Intercept)   7.274389e-01 2.474218e-02 29.400760
VOY_FREQ     -4.567822e-05 1.275484e-05 -3.581247
PRED_ENV      2.649350e-02 7.603665e-03  3.484306
ECO_DIFFTRUE -2.653286e-02 1.693410e-02 -1.566830

Getting Model ANOVA: 
Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + 
full_model:     (1 | DEST)
           Df     AIC     BIC logLik deviance  Chisq Chi Df Pr(>Chisq)   
null_model  6 -133.12 -121.90 72.563  -145.12                            
full_model  7 -139.54 -126.44 76.770  -153.54 8.4143      1   0.003723 **
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Plotting Model Coefficients: 

°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ 

Starting new analysis, with data index DIDX "10" and formula index FIDX "2" in Summary Tables.
Using formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 05_results_euk_asv00_shal_UNIF_model_data_2020-Jan-31-14-16-03_with_hon_info.csv. 

Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.
- Setting types.
- Input dimensions are: 69 17 .
- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST B_FON_NOECO B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .
- Kept variables are: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST .
- Intermediate dimensions are: 69 6 .
- Undefined rows have been removed, assuming they were real "NA" and not "0".
- Final dimensions are: 48 6 .
# A tibble: 48 x 6
   RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT  DEST 
          <dbl>    <dbl>    <dbl> <fct>    <fct> <fct>
 1        0.702        1   1.06   TRUE     AW    WL   
 2        0.634      163   1.56   TRUE     BT    AW   
 3        0.713        5   1.50   TRUE     BT    GH   
 4        0.800      304   1.52   FALSE    BT    HT   
 5        0.782        1   2.14   TRUE     BT    LB   
 6        0.760       67   3.16   FALSE    BT    MI   
 7        0.801      275   1.56   FALSE    BT    NO   
 8        0.728       60   1.57   TRUE     BT    RT   
 9        0.728      186   0.921  FALSE    BT    WL   
10        0.764      126   2.25   TRUE     BT    ZB   
11        0.836       16   1.29   FALSE    CB    PL   
12        0.683        6   0.547  FALSE    CB    RC   
13        0.673        1   1.07   TRUE     GH    WL   
14        0.741       16   2.79   TRUE     HN    CB   
15        0.840        1   2.81   TRUE     HN    HT   
16        0.779      224   2.11   TRUE     HT    AW   
17        0.765        8   2.09   TRUE     HT    GH   
18        0.889        6   2.53   TRUE     HT    LB   
19        0.849      209   2.94   FALSE    HT    MI   
20        0.632     3684   0.0459 FALSE    HT    NO   
21        0.838        1   2.74   TRUE     HT    PM   
22        0.701      127   2.23   TRUE     HT    RT   
23        0.653       13   1.55   FALSE    HT    WL   
24        0.874        4   3.39   TRUE     HT    ZB   
25        0.735       27   1.94   FALSE    LB    CB   
26        0.721        1   1.50   TRUE     LB    MI   
27        0.745       52   4.15   TRUE     MI    AW   
28        0.869       92   2.94   FALSE    MI    NO   
29        0.719        1   3.38   TRUE     MI    OK   
30        0.785        1   4.18   TRUE     MI    RT   
31        0.790        1   3.49   TRUE     MI    ZB   
32        0.603        2   1.12   TRUE     RT    WL   
33        0.796      117   2.06   TRUE     SI    AD   
34        0.741       10   4.01   TRUE     SI    AW   
35        0.743        3   3.18   TRUE     SI    BT   
36        0.737        1   3.18   TRUE     SI    CB   
37        0.797        7   3.93   TRUE     SI    GH   
38        0.761       53   0.576  TRUE     SI    HN   
39        0.842       53   2.81   TRUE     SI    HT   
40        0.727       82   1.55   TRUE     SI    LB   
41        0.858       65   2.80   TRUE     SI    NO   
42        0.706        3   3.17   TRUE     SI    OK   
43        0.841       14   3.86   TRUE     SI    PL   
44        0.781        5   2.54   TRUE     SI    PM   
45        0.699       19   2.94   TRUE     SI    RC   
46        0.777       37   4.05   TRUE     SI    RT   
47        0.777        2   3.51   TRUE     SI    ZB   
48        0.813        2   2.77   TRUE     WL    ZB   

Modelling function received variables: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .

Modelling function received variables: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .

Getting Model Summary: 
Linear mixed model fit by maximum likelihood  ['lmerMod']
Formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) +      (1 | DEST)
   Data: data_item

     AIC      BIC   logLik deviance df.resid 
  -139.5   -126.4     76.8   -153.5       41 

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-1.99915 -0.41205 -0.09242  0.58291  1.93039 

Random effects:
 Groups   Name        Variance  Std.Dev.
 DEST     (Intercept) 0.0019784 0.04448 
 PORT     (Intercept) 0.0001904 0.01380 
 Residual             0.0012387 0.03520 
Number of obs: 48, groups:  DEST, 17; PORT, 11

Fixed effects:
               Estimate Std. Error t value
(Intercept)   7.274e-01  2.474e-02  29.401
VOY_FREQ     -4.568e-05  1.275e-05  -3.581
PRED_ENV      2.649e-02  7.604e-03   3.484
ECO_DIFFTRUE -2.653e-02  1.693e-02  -1.567

Correlation of Fixed Effects:
            (Intr) VOY_FR PRED_E
VOY_FREQ    -0.393              
PRED_ENV    -0.648  0.387       
ECO_DIFFTRU -0.450  0.134 -0.120

Getting Model Coefficients from Summary: 
                  Estimate   Std. Error   t value
(Intercept)   7.274389e-01 2.474218e-02 29.400760
VOY_FREQ     -4.567822e-05 1.275484e-05 -3.581247
PRED_ENV      2.649350e-02 7.603665e-03  3.484306
ECO_DIFFTRUE -2.653286e-02 1.693410e-02 -1.566830

Getting Model ANOVA: 
Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + 
full_model:     (1 | DEST)
           Df     AIC     BIC logLik deviance  Chisq Chi Df Pr(>Chisq)   
null_model  6 -133.12 -121.90 72.563  -145.12                            
full_model  7 -139.54 -126.44 76.770  -153.54 8.4143      1   0.003723 **
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Plotting Model Coefficients: 

°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ 

Starting new analysis, with data index DIDX "11" and formula index FIDX "2" in Summary Tables.
Using formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 06_results_euk_otu99_shal_UNIF_model_data_2020-Jan-31-14-16-14_no_ph_with_hon_info.csv. 

Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.
- Setting types.
- Input dimensions are: 64 17 .
- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST B_FON_NOECO B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .
- Kept variables are: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST .
- Intermediate dimensions are: 64 6 .
- Undefined rows have been removed, assuming they were real "NA" and not "0".
- Final dimensions are: 48 6 .
# A tibble: 48 x 6
   RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT  DEST 
          <dbl>    <dbl>    <dbl> <fct>    <fct> <fct>
 1        0.716        1   1.06   TRUE     AW    WL   
 2        0.625      163   1.56   TRUE     BT    AW   
 3        0.705        5   1.50   TRUE     BT    GH   
 4        0.800      304   1.52   FALSE    BT    HT   
 5        0.777        1   2.14   TRUE     BT    LB   
 6        0.750       67   3.16   FALSE    BT    MI   
 7        0.795      275   1.56   FALSE    BT    NO   
 8        0.735       60   1.57   TRUE     BT    RT   
 9        0.733      186   0.921  FALSE    BT    WL   
10        0.776      126   2.25   TRUE     BT    ZB   
11        0.840       16   1.29   FALSE    CB    PL   
12        0.675        6   0.547  FALSE    CB    RC   
13        0.675        1   1.07   TRUE     GH    WL   
14        0.731       16   2.79   TRUE     HN    CB   
15        0.836        1   2.81   TRUE     HN    HT   
16        0.781      224   2.11   TRUE     HT    AW   
17        0.757        8   2.09   TRUE     HT    GH   
18        0.891        6   2.53   TRUE     HT    LB   
19        0.852      209   2.94   FALSE    HT    MI   
20        0.619     3684   0.0459 FALSE    HT    NO   
21        0.830        1   2.74   TRUE     HT    PM   
22        0.694      127   2.23   TRUE     HT    RT   
23        0.642       13   1.55   FALSE    HT    WL   
24        0.878        4   3.39   TRUE     HT    ZB   
25        0.724       27   1.94   FALSE    LB    CB   
26        0.710        1   1.50   TRUE     LB    MI   
27        0.745       52   4.15   TRUE     MI    AW   
28        0.870       92   2.94   FALSE    MI    NO   
29        0.706        1   3.38   TRUE     MI    OK   
30        0.793        1   4.18   TRUE     MI    RT   
31        0.801        1   3.49   TRUE     MI    ZB   
32        0.607        2   1.12   TRUE     RT    WL   
33        0.805      117   2.06   TRUE     SI    AD   
34        0.734       10   4.01   TRUE     SI    AW   
35        0.741        3   3.18   TRUE     SI    BT   
36        0.736        1   3.18   TRUE     SI    CB   
37        0.800        7   3.93   TRUE     SI    GH   
38        0.765       53   0.576  TRUE     SI    HN   
39        0.843       53   2.81   TRUE     SI    HT   
40        0.721       82   1.55   TRUE     SI    LB   
41        0.857       65   2.80   TRUE     SI    NO   
42        0.706        3   3.17   TRUE     SI    OK   
43        0.843       14   3.86   TRUE     SI    PL   
44        0.784        5   2.54   TRUE     SI    PM   
45        0.692       19   2.94   TRUE     SI    RC   
46        0.777       37   4.05   TRUE     SI    RT   
47        0.777        2   3.51   TRUE     SI    ZB   
48        0.830        2   2.77   TRUE     WL    ZB   

Modelling function received variables: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .

Modelling function received variables: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Getting Model Summary: 
Linear mixed model fit by maximum likelihood  ['lmerMod']
Formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) +      (1 | DEST)
   Data: data_item

     AIC      BIC   logLik deviance df.resid 
  -131.9   -118.8     72.9   -145.9       41 

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-1.93157 -0.46344 -0.07663  0.61013  2.03076 

Random effects:
 Groups   Name        Variance  Std.Dev.
 DEST     (Intercept) 0.0019728 0.044416
 PORT     (Intercept) 0.0000957 0.009782
 Residual             0.0016357 0.040444
Number of obs: 48, groups:  DEST, 17; PORT, 11

Fixed effects:
               Estimate Std. Error t value
(Intercept)   7.253e-01  2.575e-02  28.164
VOY_FREQ     -4.423e-05  1.425e-05  -3.104
PRED_ENV      2.646e-02  8.096e-03   3.269
ECO_DIFFTRUE -2.632e-02  1.859e-02  -1.416

Correlation of Fixed Effects:
            (Intr) VOY_FR PRED_E
VOY_FREQ    -0.402              
PRED_ENV    -0.649  0.370       
ECO_DIFFTRU -0.432  0.129 -0.180

Getting Model Coefficients from Summary: 
                  Estimate   Std. Error   t value
(Intercept)   7.253403e-01 2.575375e-02 28.164451
VOY_FREQ     -4.422621e-05 1.424872e-05 -3.103873
PRED_ENV      2.646313e-02 8.095769e-03  3.268760
ECO_DIFFTRUE -2.631902e-02 1.858502e-02 -1.416141

Getting Model ANOVA: 
Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + 
full_model:     (1 | DEST)
           Df     AIC     BIC logLik deviance  Chisq Chi Df Pr(>Chisq)   
null_model  6 -126.93 -115.70 69.464  -138.93                            
full_model  7 -131.86 -118.76 72.930  -145.86 6.9323      1   0.008465 **
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Plotting Model Coefficients: 

°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ 

Starting new analysis, with data index DIDX "12" and formula index FIDX "2" in Summary Tables.
Using formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 06_results_euk_otu99_shal_UNIF_model_data_2020-Jan-31-14-16-14_with_hon_info.csv. 

Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.
- Setting types.
- Input dimensions are: 69 17 .
- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST B_FON_NOECO B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .
- Kept variables are: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST .
- Intermediate dimensions are: 69 6 .
- Undefined rows have been removed, assuming they were real "NA" and not "0".
- Final dimensions are: 48 6 .
# A tibble: 48 x 6
   RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT  DEST 
          <dbl>    <dbl>    <dbl> <fct>    <fct> <fct>
 1        0.716        1   1.06   TRUE     AW    WL   
 2        0.625      163   1.56   TRUE     BT    AW   
 3        0.705        5   1.50   TRUE     BT    GH   
 4        0.800      304   1.52   FALSE    BT    HT   
 5        0.777        1   2.14   TRUE     BT    LB   
 6        0.750       67   3.16   FALSE    BT    MI   
 7        0.795      275   1.56   FALSE    BT    NO   
 8        0.735       60   1.57   TRUE     BT    RT   
 9        0.733      186   0.921  FALSE    BT    WL   
10        0.776      126   2.25   TRUE     BT    ZB   
11        0.840       16   1.29   FALSE    CB    PL   
12        0.675        6   0.547  FALSE    CB    RC   
13        0.675        1   1.07   TRUE     GH    WL   
14        0.731       16   2.79   TRUE     HN    CB   
15        0.836        1   2.81   TRUE     HN    HT   
16        0.781      224   2.11   TRUE     HT    AW   
17        0.757        8   2.09   TRUE     HT    GH   
18        0.891        6   2.53   TRUE     HT    LB   
19        0.852      209   2.94   FALSE    HT    MI   
20        0.619     3684   0.0459 FALSE    HT    NO   
21        0.830        1   2.74   TRUE     HT    PM   
22        0.694      127   2.23   TRUE     HT    RT   
23        0.642       13   1.55   FALSE    HT    WL   
24        0.878        4   3.39   TRUE     HT    ZB   
25        0.724       27   1.94   FALSE    LB    CB   
26        0.710        1   1.50   TRUE     LB    MI   
27        0.745       52   4.15   TRUE     MI    AW   
28        0.870       92   2.94   FALSE    MI    NO   
29        0.706        1   3.38   TRUE     MI    OK   
30        0.793        1   4.18   TRUE     MI    RT   
31        0.801        1   3.49   TRUE     MI    ZB   
32        0.607        2   1.12   TRUE     RT    WL   
33        0.805      117   2.06   TRUE     SI    AD   
34        0.734       10   4.01   TRUE     SI    AW   
35        0.741        3   3.18   TRUE     SI    BT   
36        0.736        1   3.18   TRUE     SI    CB   
37        0.800        7   3.93   TRUE     SI    GH   
38        0.765       53   0.576  TRUE     SI    HN   
39        0.843       53   2.81   TRUE     SI    HT   
40        0.721       82   1.55   TRUE     SI    LB   
41        0.857       65   2.80   TRUE     SI    NO   
42        0.706        3   3.17   TRUE     SI    OK   
43        0.843       14   3.86   TRUE     SI    PL   
44        0.784        5   2.54   TRUE     SI    PM   
45        0.692       19   2.94   TRUE     SI    RC   
46        0.777       37   4.05   TRUE     SI    RT   
47        0.777        2   3.51   TRUE     SI    ZB   
48        0.830        2   2.77   TRUE     WL    ZB   

Modelling function received variables: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .

Modelling function received variables: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Getting Model Summary: 
Linear mixed model fit by maximum likelihood  ['lmerMod']
Formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) +      (1 | DEST)
   Data: data_item

     AIC      BIC   logLik deviance df.resid 
  -131.9   -118.8     72.9   -145.9       41 

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-1.93157 -0.46344 -0.07663  0.61013  2.03076 

Random effects:
 Groups   Name        Variance  Std.Dev.
 DEST     (Intercept) 0.0019728 0.044416
 PORT     (Intercept) 0.0000957 0.009782
 Residual             0.0016357 0.040444
Number of obs: 48, groups:  DEST, 17; PORT, 11

Fixed effects:
               Estimate Std. Error t value
(Intercept)   7.253e-01  2.575e-02  28.164
VOY_FREQ     -4.423e-05  1.425e-05  -3.104
PRED_ENV      2.646e-02  8.096e-03   3.269
ECO_DIFFTRUE -2.632e-02  1.859e-02  -1.416

Correlation of Fixed Effects:
            (Intr) VOY_FR PRED_E
VOY_FREQ    -0.402              
PRED_ENV    -0.649  0.370       
ECO_DIFFTRU -0.432  0.129 -0.180

Getting Model Coefficients from Summary: 
                  Estimate   Std. Error   t value
(Intercept)   7.253403e-01 2.575375e-02 28.164451
VOY_FREQ     -4.422621e-05 1.424872e-05 -3.103873
PRED_ENV      2.646313e-02 8.095769e-03  3.268760
ECO_DIFFTRUE -2.631902e-02 1.858502e-02 -1.416141

Getting Model ANOVA: 
Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + 
full_model:     (1 | DEST)
           Df     AIC     BIC logLik deviance  Chisq Chi Df Pr(>Chisq)   
null_model  6 -126.93 -115.70 69.464  -138.93                            
full_model  7 -131.86 -118.76 72.930  -145.86 6.9323      1   0.008465 **
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Plotting Model Coefficients: 

°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ 

Starting new analysis, with data index DIDX "13" and formula index FIDX "2" in Summary Tables.
Using formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 07_results_euk_asv00_shal_JAQU_model_data_2020-Jan-31-14-16-25_no_ph_with_hon_info.csv. 

Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.
- Setting types.
- Input dimensions are: 64 17 .
- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST B_FON_NOECO B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .
- Kept variables are: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST .
- Intermediate dimensions are: 64 6 .
- Undefined rows have been removed, assuming they were real "NA" and not "0".
- Final dimensions are: 48 6 .
# A tibble: 48 x 6
   RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT  DEST 
          <dbl>    <dbl>    <dbl> <fct>    <fct> <fct>
 1        0.951        1   1.06   TRUE     AW    WL   
 2        0.902      163   1.56   TRUE     BT    AW   
 3        0.943        5   1.50   TRUE     BT    GH   
 4        0.989      304   1.52   FALSE    BT    HT   
 5        0.964        1   2.14   TRUE     BT    LB   
 6        0.974       67   3.16   FALSE    BT    MI   
 7        0.985      275   1.56   FALSE    BT    NO   
 8        0.955       60   1.57   TRUE     BT    RT   
 9        0.957      186   0.921  FALSE    BT    WL   
10        0.956      126   2.25   TRUE     BT    ZB   
11        1           16   1.29   FALSE    CB    PL   
12        0.907        6   0.547  FALSE    CB    RC   
13        0.913        1   1.07   TRUE     GH    WL   
14        0.980       16   2.79   TRUE     HN    CB   
15        0.993        1   2.81   TRUE     HN    HT   
16        0.988      224   2.11   TRUE     HT    AW   
17        0.969        8   2.09   TRUE     HT    GH   
18        1.00         6   2.53   TRUE     HT    LB   
19        0.994      209   2.94   FALSE    HT    MI   
20        0.891     3684   0.0459 FALSE    HT    NO   
21        0.997        1   2.74   TRUE     HT    PM   
22        0.948      127   2.23   TRUE     HT    RT   
23        0.905       13   1.55   FALSE    HT    WL   
24        0.998        4   3.39   TRUE     HT    ZB   
25        0.939       27   1.94   FALSE    LB    CB   
26        0.942        1   1.50   TRUE     LB    MI   
27        0.988       52   4.15   TRUE     MI    AW   
28        0.998       92   2.94   FALSE    MI    NO   
29        0.962        1   3.38   TRUE     MI    OK   
30        0.991        1   4.18   TRUE     MI    RT   
31        0.988        1   3.49   TRUE     MI    ZB   
32        0.894        2   1.12   TRUE     RT    WL   
33        0.971      117   2.06   TRUE     SI    AD   
34        0.986       10   4.01   TRUE     SI    AW   
35        0.973        3   3.18   TRUE     SI    BT   
36        0.982        1   3.18   TRUE     SI    CB   
37        0.996        7   3.93   TRUE     SI    GH   
38        0.958       53   0.576  TRUE     SI    HN   
39        0.997       53   2.81   TRUE     SI    HT   
40        0.966       82   1.55   TRUE     SI    LB   
41        0.998       65   2.80   TRUE     SI    NO   
42        0.959        3   3.17   TRUE     SI    OK   
43        0.998       14   3.86   TRUE     SI    PL   
44        0.996        5   2.54   TRUE     SI    PM   
45        0.967       19   2.94   TRUE     SI    RC   
46        0.993       37   4.05   TRUE     SI    RT   
47        0.986        2   3.51   TRUE     SI    ZB   
48        0.996        2   2.77   TRUE     WL    ZB   

Modelling function received variables: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Modelling function received variables: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Getting Model Summary: 
Linear mixed model fit by maximum likelihood  ['lmerMod']
Formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) +      (1 | DEST)
   Data: data_item

     AIC      BIC   logLik deviance df.resid 
  -219.7   -206.6    116.9   -233.7       41 

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-2.30113 -0.47225 -0.00788  0.61214  1.87274 

Random effects:
 Groups   Name        Variance  Std.Dev.
 DEST     (Intercept) 0.0001487 0.0122  
 PORT     (Intercept) 0.0000000 0.0000  
 Residual             0.0003422 0.0185  
Number of obs: 48, groups:  DEST, 17; PORT, 11

Fixed effects:
               Estimate Std. Error t value
(Intercept)   9.342e-01  9.847e-03  94.873
VOY_FREQ     -1.361e-05  6.145e-06  -2.215
PRED_ENV      1.797e-02  3.291e-03   5.459
ECO_DIFFTRUE -8.052e-03  7.807e-03  -1.031

Correlation of Fixed Effects:
            (Intr) VOY_FR PRED_E
VOY_FREQ    -0.432              
PRED_ENV    -0.668  0.335       
ECO_DIFFTRU -0.425  0.149 -0.253
convergence code: 0
boundary (singular) fit: see ?isSingular

Getting Model Coefficients from Summary: 
                  Estimate   Std. Error   t value
(Intercept)   0.9342176247 9.847000e-03 94.873325
VOY_FREQ     -0.0000136088 6.144506e-06 -2.214791
PRED_ENV      0.0179682904 3.291389e-03  5.459181
ECO_DIFFTRUE -0.0080519710 7.807465e-03 -1.031317

Getting Model ANOVA: 
Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + 
full_model:     (1 | DEST)
           Df     AIC     BIC logLik deviance  Chisq Chi Df Pr(>Chisq)  
null_model  6 -217.57 -206.34 114.79  -229.57                           
full_model  7 -219.74 -206.64 116.87  -233.74 4.1689      1    0.04117 *
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Plotting Model Coefficients: 
boundary (singular) fit: see ?isSingular

°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ 

Starting new analysis, with data index DIDX "14" and formula index FIDX "2" in Summary Tables.
Using formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 07_results_euk_asv00_shal_JAQU_model_data_2020-Jan-31-14-16-25_with_hon_info.csv. 

Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.
- Setting types.
- Input dimensions are: 69 17 .
- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST B_FON_NOECO B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .
- Kept variables are: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST .
- Intermediate dimensions are: 69 6 .
- Undefined rows have been removed, assuming they were real "NA" and not "0".
- Final dimensions are: 48 6 .
# A tibble: 48 x 6
   RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT  DEST 
          <dbl>    <dbl>    <dbl> <fct>    <fct> <fct>
 1        0.951        1   1.06   TRUE     AW    WL   
 2        0.902      163   1.56   TRUE     BT    AW   
 3        0.943        5   1.50   TRUE     BT    GH   
 4        0.989      304   1.52   FALSE    BT    HT   
 5        0.964        1   2.14   TRUE     BT    LB   
 6        0.974       67   3.16   FALSE    BT    MI   
 7        0.985      275   1.56   FALSE    BT    NO   
 8        0.955       60   1.57   TRUE     BT    RT   
 9        0.957      186   0.921  FALSE    BT    WL   
10        0.956      126   2.25   TRUE     BT    ZB   
11        1           16   1.29   FALSE    CB    PL   
12        0.907        6   0.547  FALSE    CB    RC   
13        0.913        1   1.07   TRUE     GH    WL   
14        0.980       16   2.79   TRUE     HN    CB   
15        0.993        1   2.81   TRUE     HN    HT   
16        0.988      224   2.11   TRUE     HT    AW   
17        0.969        8   2.09   TRUE     HT    GH   
18        1.00         6   2.53   TRUE     HT    LB   
19        0.994      209   2.94   FALSE    HT    MI   
20        0.891     3684   0.0459 FALSE    HT    NO   
21        0.997        1   2.74   TRUE     HT    PM   
22        0.948      127   2.23   TRUE     HT    RT   
23        0.905       13   1.55   FALSE    HT    WL   
24        0.998        4   3.39   TRUE     HT    ZB   
25        0.939       27   1.94   FALSE    LB    CB   
26        0.942        1   1.50   TRUE     LB    MI   
27        0.988       52   4.15   TRUE     MI    AW   
28        0.998       92   2.94   FALSE    MI    NO   
29        0.962        1   3.38   TRUE     MI    OK   
30        0.991        1   4.18   TRUE     MI    RT   
31        0.988        1   3.49   TRUE     MI    ZB   
32        0.894        2   1.12   TRUE     RT    WL   
33        0.971      117   2.06   TRUE     SI    AD   
34        0.986       10   4.01   TRUE     SI    AW   
35        0.973        3   3.18   TRUE     SI    BT   
36        0.982        1   3.18   TRUE     SI    CB   
37        0.996        7   3.93   TRUE     SI    GH   
38        0.958       53   0.576  TRUE     SI    HN   
39        0.997       53   2.81   TRUE     SI    HT   
40        0.966       82   1.55   TRUE     SI    LB   
41        0.998       65   2.80   TRUE     SI    NO   
42        0.959        3   3.17   TRUE     SI    OK   
43        0.998       14   3.86   TRUE     SI    PL   
44        0.996        5   2.54   TRUE     SI    PM   
45        0.967       19   2.94   TRUE     SI    RC   
46        0.993       37   4.05   TRUE     SI    RT   
47        0.986        2   3.51   TRUE     SI    ZB   
48        0.996        2   2.77   TRUE     WL    ZB   

Modelling function received variables: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Modelling function received variables: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Getting Model Summary: 
Linear mixed model fit by maximum likelihood  ['lmerMod']
Formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) +      (1 | DEST)
   Data: data_item

     AIC      BIC   logLik deviance df.resid 
  -219.7   -206.6    116.9   -233.7       41 

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-2.30113 -0.47225 -0.00788  0.61214  1.87274 

Random effects:
 Groups   Name        Variance  Std.Dev.
 DEST     (Intercept) 0.0001487 0.0122  
 PORT     (Intercept) 0.0000000 0.0000  
 Residual             0.0003422 0.0185  
Number of obs: 48, groups:  DEST, 17; PORT, 11

Fixed effects:
               Estimate Std. Error t value
(Intercept)   9.342e-01  9.847e-03  94.873
VOY_FREQ     -1.361e-05  6.145e-06  -2.215
PRED_ENV      1.797e-02  3.291e-03   5.459
ECO_DIFFTRUE -8.052e-03  7.807e-03  -1.031

Correlation of Fixed Effects:
            (Intr) VOY_FR PRED_E
VOY_FREQ    -0.432              
PRED_ENV    -0.668  0.335       
ECO_DIFFTRU -0.425  0.149 -0.253
convergence code: 0
boundary (singular) fit: see ?isSingular

Getting Model Coefficients from Summary: 
                  Estimate   Std. Error   t value
(Intercept)   0.9342176247 9.847000e-03 94.873325
VOY_FREQ     -0.0000136088 6.144506e-06 -2.214791
PRED_ENV      0.0179682904 3.291389e-03  5.459181
ECO_DIFFTRUE -0.0080519710 7.807465e-03 -1.031317

Getting Model ANOVA: 
Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + 
full_model:     (1 | DEST)
           Df     AIC     BIC logLik deviance  Chisq Chi Df Pr(>Chisq)  
null_model  6 -217.57 -206.34 114.79  -229.57                           
full_model  7 -219.74 -206.64 116.87  -233.74 4.1689      1    0.04117 *
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Plotting Model Coefficients: 
boundary (singular) fit: see ?isSingular

°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ 

Starting new analysis, with data index DIDX "15" and formula index FIDX "2" in Summary Tables.
Using formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 08_results_euk_otu99_shal_JAQU_model_data_2020-Jan-31-14-16-36_no_ph_with_hon_info.csv. 

Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.
- Setting types.
- Input dimensions are: 64 17 .
- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST B_FON_NOECO B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .
- Kept variables are: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST .
- Intermediate dimensions are: 64 6 .
- Undefined rows have been removed, assuming they were real "NA" and not "0".
- Final dimensions are: 48 6 .
# A tibble: 48 x 6
   RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT  DEST 
          <dbl>    <dbl>    <dbl> <fct>    <fct> <fct>
 1        0.919        1   1.06   TRUE     AW    WL   
 2        0.847      163   1.56   TRUE     BT    AW   
 3        0.906        5   1.50   TRUE     BT    GH   
 4        0.980      304   1.52   FALSE    BT    HT   
 5        0.936        1   2.14   TRUE     BT    LB   
 6        0.933       67   3.16   FALSE    BT    MI   
 7        0.971      275   1.56   FALSE    BT    NO   
 8        0.927       60   1.57   TRUE     BT    RT   
 9        0.934      186   0.921  FALSE    BT    WL   
10        0.931      126   2.25   TRUE     BT    ZB   
11        0.998       16   1.29   FALSE    CB    PL   
12        0.863        6   0.547  FALSE    CB    RC   
13        0.879        1   1.07   TRUE     GH    WL   
14        0.947       16   2.79   TRUE     HN    CB   
15        0.988        1   2.81   TRUE     HN    HT   
16        0.981      224   2.11   TRUE     HT    AW   
17        0.951        8   2.09   TRUE     HT    GH   
18        0.998        6   2.53   TRUE     HT    LB   
19        0.987      209   2.94   FALSE    HT    MI   
20        0.848     3684   0.0459 FALSE    HT    NO   
21        0.995        1   2.74   TRUE     HT    PM   
22        0.920      127   2.23   TRUE     HT    RT   
23        0.865       13   1.55   FALSE    HT    WL   
24        0.997        4   3.39   TRUE     HT    ZB   
25        0.901       27   1.94   FALSE    LB    CB   
26        0.907        1   1.50   TRUE     LB    MI   
27        0.966       52   4.15   TRUE     MI    AW   
28        0.990       92   2.94   FALSE    MI    NO   
29        0.927        1   3.38   TRUE     MI    OK   
30        0.976        1   4.18   TRUE     MI    RT   
31        0.965        1   3.49   TRUE     MI    ZB   
32        0.839        2   1.12   TRUE     RT    WL   
33        0.945      117   2.06   TRUE     SI    AD   
34        0.960       10   4.01   TRUE     SI    AW   
35        0.947        3   3.18   TRUE     SI    BT   
36        0.950        1   3.18   TRUE     SI    CB   
37        0.986        7   3.93   TRUE     SI    GH   
38        0.935       53   0.576  TRUE     SI    HN   
39        0.994       53   2.81   TRUE     SI    HT   
40        0.931       82   1.55   TRUE     SI    LB   
41        0.995       65   2.80   TRUE     SI    NO   
42        0.919        3   3.17   TRUE     SI    OK   
43        0.997       14   3.86   TRUE     SI    PL   
44        0.983        5   2.54   TRUE     SI    PM   
45        0.928       19   2.94   TRUE     SI    RC   
46        0.976       37   4.05   TRUE     SI    RT   
47        0.952        2   3.51   TRUE     SI    ZB   
48        0.987        2   2.77   TRUE     WL    ZB   

Modelling function received variables: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Modelling function received variables: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Getting Model Summary: 
Linear mixed model fit by maximum likelihood  ['lmerMod']
Formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) +      (1 | DEST)
   Data: data_item

     AIC      BIC   logLik deviance df.resid 
  -177.9   -164.8     96.0   -191.9       41 

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-2.29315 -0.46458 -0.09173  0.67273  2.09969 

Random effects:
 Groups   Name        Variance  Std.Dev.
 DEST     (Intercept) 0.0004305 0.02075 
 PORT     (Intercept) 0.0000000 0.00000 
 Residual             0.0007803 0.02793 
Number of obs: 48, groups:  DEST, 17; PORT, 11

Fixed effects:
               Estimate Std. Error t value
(Intercept)   9.033e-01  1.524e-02  59.256
VOY_FREQ     -1.883e-05  9.366e-06  -2.011
PRED_ENV      2.296e-02  5.046e-03   4.551
ECO_DIFFTRUE -1.269e-02  1.194e-02  -1.063

Correlation of Fixed Effects:
            (Intr) VOY_FR PRED_E
VOY_FREQ    -0.426              
PRED_ENV    -0.662  0.341       
ECO_DIFFTRU -0.422  0.141 -0.251
convergence code: 0
boundary (singular) fit: see ?isSingular

Getting Model Coefficients from Summary: 
                  Estimate   Std. Error   t value
(Intercept)   9.033103e-01 1.524425e-02 59.255809
VOY_FREQ     -1.883326e-05 9.365648e-06 -2.010887
PRED_ENV      2.296424e-02 5.045690e-03  4.551258
ECO_DIFFTRUE -1.268940e-02 1.194015e-02 -1.062751

Getting Model ANOVA: 
Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + 
full_model:     (1 | DEST)
           Df     AIC     BIC logLik deviance  Chisq Chi Df Pr(>Chisq)  
null_model  6 -176.43 -165.20 94.215  -188.43                           
full_model  7 -177.94 -164.84 95.970  -191.94 3.5104      1    0.06099 .
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Plotting Model Coefficients: 
boundary (singular) fit: see ?isSingular

°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ 

Starting new analysis, with data index DIDX "16" and formula index FIDX "2" in Summary Tables.
Using formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 08_results_euk_otu99_shal_JAQU_model_data_2020-Jan-31-14-16-36_with_hon_info.csv. 

Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.
- Setting types.
- Input dimensions are: 69 17 .
- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST B_FON_NOECO B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .
- Kept variables are: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST .
- Intermediate dimensions are: 69 6 .
- Undefined rows have been removed, assuming they were real "NA" and not "0".
- Final dimensions are: 48 6 .
# A tibble: 48 x 6
   RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT  DEST 
          <dbl>    <dbl>    <dbl> <fct>    <fct> <fct>
 1        0.919        1   1.06   TRUE     AW    WL   
 2        0.847      163   1.56   TRUE     BT    AW   
 3        0.906        5   1.50   TRUE     BT    GH   
 4        0.980      304   1.52   FALSE    BT    HT   
 5        0.936        1   2.14   TRUE     BT    LB   
 6        0.933       67   3.16   FALSE    BT    MI   
 7        0.971      275   1.56   FALSE    BT    NO   
 8        0.927       60   1.57   TRUE     BT    RT   
 9        0.934      186   0.921  FALSE    BT    WL   
10        0.931      126   2.25   TRUE     BT    ZB   
11        0.998       16   1.29   FALSE    CB    PL   
12        0.863        6   0.547  FALSE    CB    RC   
13        0.879        1   1.07   TRUE     GH    WL   
14        0.947       16   2.79   TRUE     HN    CB   
15        0.988        1   2.81   TRUE     HN    HT   
16        0.981      224   2.11   TRUE     HT    AW   
17        0.951        8   2.09   TRUE     HT    GH   
18        0.998        6   2.53   TRUE     HT    LB   
19        0.987      209   2.94   FALSE    HT    MI   
20        0.848     3684   0.0459 FALSE    HT    NO   
21        0.995        1   2.74   TRUE     HT    PM   
22        0.920      127   2.23   TRUE     HT    RT   
23        0.865       13   1.55   FALSE    HT    WL   
24        0.997        4   3.39   TRUE     HT    ZB   
25        0.901       27   1.94   FALSE    LB    CB   
26        0.907        1   1.50   TRUE     LB    MI   
27        0.966       52   4.15   TRUE     MI    AW   
28        0.990       92   2.94   FALSE    MI    NO   
29        0.927        1   3.38   TRUE     MI    OK   
30        0.976        1   4.18   TRUE     MI    RT   
31        0.965        1   3.49   TRUE     MI    ZB   
32        0.839        2   1.12   TRUE     RT    WL   
33        0.945      117   2.06   TRUE     SI    AD   
34        0.960       10   4.01   TRUE     SI    AW   
35        0.947        3   3.18   TRUE     SI    BT   
36        0.950        1   3.18   TRUE     SI    CB   
37        0.986        7   3.93   TRUE     SI    GH   
38        0.935       53   0.576  TRUE     SI    HN   
39        0.994       53   2.81   TRUE     SI    HT   
40        0.931       82   1.55   TRUE     SI    LB   
41        0.995       65   2.80   TRUE     SI    NO   
42        0.919        3   3.17   TRUE     SI    OK   
43        0.997       14   3.86   TRUE     SI    PL   
44        0.983        5   2.54   TRUE     SI    PM   
45        0.928       19   2.94   TRUE     SI    RC   
46        0.976       37   4.05   TRUE     SI    RT   
47        0.952        2   3.51   TRUE     SI    ZB   
48        0.987        2   2.77   TRUE     WL    ZB   

Modelling function received variables: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Modelling function received variables: RESP_UNIFRAC VOY_FREQ PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Getting Model Summary: 
Linear mixed model fit by maximum likelihood  ['lmerMod']
Formula: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) +      (1 | DEST)
   Data: data_item

     AIC      BIC   logLik deviance df.resid 
  -177.9   -164.8     96.0   -191.9       41 

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-2.29315 -0.46458 -0.09173  0.67273  2.09969 

Random effects:
 Groups   Name        Variance  Std.Dev.
 DEST     (Intercept) 0.0004305 0.02075 
 PORT     (Intercept) 0.0000000 0.00000 
 Residual             0.0007803 0.02793 
Number of obs: 48, groups:  DEST, 17; PORT, 11

Fixed effects:
               Estimate Std. Error t value
(Intercept)   9.033e-01  1.524e-02  59.256
VOY_FREQ     -1.883e-05  9.366e-06  -2.011
PRED_ENV      2.296e-02  5.046e-03   4.551
ECO_DIFFTRUE -1.269e-02  1.194e-02  -1.063

Correlation of Fixed Effects:
            (Intr) VOY_FR PRED_E
VOY_FREQ    -0.426              
PRED_ENV    -0.662  0.341       
ECO_DIFFTRU -0.422  0.141 -0.251
convergence code: 0
boundary (singular) fit: see ?isSingular

Getting Model Coefficients from Summary: 
                  Estimate   Std. Error   t value
(Intercept)   9.033103e-01 1.524425e-02 59.255809
VOY_FREQ     -1.883326e-05 9.365648e-06 -2.010887
PRED_ENV      2.296424e-02 5.045690e-03  4.551258
ECO_DIFFTRUE -1.268940e-02 1.194015e-02 -1.062751

Getting Model ANOVA: 
Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + 
full_model:     (1 | DEST)
           Df     AIC     BIC logLik deviance  Chisq Chi Df Pr(>Chisq)  
null_model  6 -176.43 -165.20 94.215  -188.43                           
full_model  7 -177.94 -164.84 95.970  -191.94 3.5104      1    0.06099 .
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Plotting Model Coefficients: 
boundary (singular) fit: see ?isSingular

°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ 

Starting new analysis, with data index DIDX "1" and formula index FIDX "3" in Summary Tables.
Using formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 01_results_euk_asv00_deep_UNIF_model_data_2020-Jan-31-14-15-13_no_ph_with_hon_info.csv. 

Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.
- Setting types.
- Input dimensions are: 64 17 .
- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST VOY_FREQ B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .
- Kept variables are: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST .
- Intermediate dimensions are: 64 6 .
- Undefined rows have been removed, assuming they were real "NA" and not "0".
- Final dimensions are: 48 6 .
# A tibble: 48 x 6
   RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT  DEST 
          <dbl>       <dbl>    <dbl> <fct>    <fct> <fct>
 1        0.700    0.         1.06   TRUE     AW    WL   
 2        0.636    6.06e- 1   1.56   TRUE     BT    AW   
 3        0.695    7.58e- 2   1.50   TRUE     BT    GH   
 4        0.794    4.83e- 2   1.52   FALSE    BT    HT   
 5        0.785    8.92e- 4   2.14   TRUE     BT    LB   
 6        0.756    6.07e- 8   3.16   FALSE    BT    MI   
 7        0.794    1.83e- 1   1.56   FALSE    BT    NO   
 8        0.732    5.48e- 1   1.57   TRUE     BT    RT   
 9        0.723    9.60e- 1   0.921  FALSE    BT    WL   
10        0.776    5.75e- 2   2.25   TRUE     BT    ZB   
11        0.832    2.28e- 2   1.29   FALSE    CB    PL   
12        0.683    2.07e- 4   0.547  FALSE    CB    RC   
13        0.654    1.23e- 2   1.07   TRUE     GH    WL   
14        0.739    1.00e-11   2.79   TRUE     HN    CB   
15        0.829    0.         2.81   TRUE     HN    HT   
16        0.774    8.75e- 6   2.11   TRUE     HT    AW   
17        0.747    2.48e- 6   2.09   TRUE     HT    GH   
18        0.885    3.08e- 4   2.53   TRUE     HT    LB   
19        0.845    3.56e- 4   2.94   FALSE    HT    MI   
20        0.628    1.00e+ 0   0.0459 FALSE    HT    NO   
21        0.828    0.         2.74   TRUE     HT    PM   
22        0.695    3.59e- 6   2.23   TRUE     HT    RT   
23        0.639    7.49e- 4   1.55   FALSE    HT    WL   
24        0.869    2.57e-10   3.39   TRUE     HT    ZB   
25        0.738    8.05e- 6   1.94   FALSE    LB    CB   
26        0.726    3.73e- 4   1.50   TRUE     LB    MI   
27        0.748    0.         4.15   TRUE     MI    AW   
28        0.864    3.18e- 4   2.94   FALSE    MI    NO   
29        0.712    1.97e-11   3.38   TRUE     MI    OK   
30        0.786    0.         4.18   TRUE     MI    RT   
31        0.799    8.88e-16   3.49   TRUE     MI    ZB   
32        0.603    5.46e- 2   1.12   TRUE     RT    WL   
33        0.815    4.36e- 4   2.06   TRUE     SI    AD   
34        0.740    5.55e-16   4.01   TRUE     SI    AW   
35        0.748    4.13e- 9   3.18   TRUE     SI    BT   
36        0.724    9.94e-12   3.18   TRUE     SI    CB   
37        0.789    2.11e-15   3.93   TRUE     SI    GH   
38        0.762    7.13e- 1   0.576  TRUE     SI    HN   
39        0.836    6.99e- 4   2.81   TRUE     SI    HT   
40        0.730    6.40e- 3   1.55   TRUE     SI    LB   
41        0.853    4.30e- 3   2.80   TRUE     SI    NO   
42        0.692    1.91e-10   3.17   TRUE     SI    OK   
43        0.835    4.25e-14   3.86   TRUE     SI    PL   
44        0.780    3.82e- 9   2.54   TRUE     SI    PM   
45        0.691    1.30e- 8   2.94   TRUE     SI    RC   
46        0.774    2.22e-16   4.05   TRUE     SI    RT   
47        0.789    1.11e-15   3.51   TRUE     SI    ZB   
48        0.817    8.20e- 6   2.77   TRUE     WL    ZB   

Modelling function received variables: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Modelling function received variables: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Getting Model Summary: 
Linear mixed model fit by maximum likelihood  ['lmerMod']
Formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) +      (1 | DEST)
   Data: data_item

     AIC      BIC   logLik deviance df.resid 
  -132.0   -118.9     73.0   -146.0       41 

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-1.89659 -0.60863 -0.08118  0.72351  2.01209 

Random effects:
 Groups   Name        Variance  Std.Dev. 
 DEST     (Intercept) 1.577e-03 3.971e-02
 PORT     (Intercept) 3.178e-19 5.637e-10
 Residual             1.845e-03 4.295e-02
Number of obs: 48, groups:  DEST, 17; PORT, 11

Fixed effects:
              Estimate Std. Error t value
(Intercept)   0.715394   0.026757  26.737
B_FON_NOECO  -0.033056   0.034093  -0.970
PRED_ENV      0.028324   0.008562   3.308
ECO_DIFFTRUE -0.024783   0.018732  -1.323

Correlation of Fixed Effects:
            (Intr) B_FON_ PRED_E
B_FON_NOECO -0.544              
PRED_ENV    -0.698  0.491       
ECO_DIFFTRU -0.391  0.107 -0.220
convergence code: 0
boundary (singular) fit: see ?isSingular

Getting Model Coefficients from Summary: 
                Estimate  Std. Error    t value
(Intercept)   0.71539350 0.026756703 26.7369828
B_FON_NOECO  -0.03305617 0.034092787 -0.9695942
PRED_ENV      0.02832402 0.008561583  3.3082693
ECO_DIFFTRUE -0.02478349 0.018732139 -1.3230462

Getting Model ANOVA: 
Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + 
full_model:     (1 | DEST)
           Df     AIC     BIC logLik deviance  Chisq Chi Df Pr(>Chisq)
null_model  6 -133.05 -121.82 72.525  -145.05                         
full_model  7 -131.97 -118.88 72.988  -145.97 0.9261      1     0.3359

Plotting Model Coefficients: 
boundary (singular) fit: see ?isSingular

°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ 

Starting new analysis, with data index DIDX "2" and formula index FIDX "3" in Summary Tables.
Using formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 01_results_euk_asv00_deep_UNIF_model_data_2020-Jan-31-14-15-13_with_hon_info.csv. 

Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.
- Setting types.
- Input dimensions are: 69 17 .
- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST VOY_FREQ B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .
- Kept variables are: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST .
- Intermediate dimensions are: 69 6 .
- Undefined rows have been removed, assuming they were real "NA" and not "0".
- Final dimensions are: 48 6 .
# A tibble: 48 x 6
   RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT  DEST 
          <dbl>       <dbl>    <dbl> <fct>    <fct> <fct>
 1        0.700    0.         1.06   TRUE     AW    WL   
 2        0.636    6.06e- 1   1.56   TRUE     BT    AW   
 3        0.695    7.58e- 2   1.50   TRUE     BT    GH   
 4        0.794    4.83e- 2   1.52   FALSE    BT    HT   
 5        0.785    8.92e- 4   2.14   TRUE     BT    LB   
 6        0.756    6.07e- 8   3.16   FALSE    BT    MI   
 7        0.794    1.83e- 1   1.56   FALSE    BT    NO   
 8        0.732    5.48e- 1   1.57   TRUE     BT    RT   
 9        0.723    9.60e- 1   0.921  FALSE    BT    WL   
10        0.776    5.75e- 2   2.25   TRUE     BT    ZB   
11        0.832    2.28e- 2   1.29   FALSE    CB    PL   
12        0.683    2.07e- 4   0.547  FALSE    CB    RC   
13        0.654    1.23e- 2   1.07   TRUE     GH    WL   
14        0.739    1.00e-11   2.79   TRUE     HN    CB   
15        0.829    0.         2.81   TRUE     HN    HT   
16        0.774    8.75e- 6   2.11   TRUE     HT    AW   
17        0.747    2.48e- 6   2.09   TRUE     HT    GH   
18        0.885    3.08e- 4   2.53   TRUE     HT    LB   
19        0.845    3.56e- 4   2.94   FALSE    HT    MI   
20        0.628    1.00e+ 0   0.0459 FALSE    HT    NO   
21        0.828    0.         2.74   TRUE     HT    PM   
22        0.695    3.59e- 6   2.23   TRUE     HT    RT   
23        0.639    7.49e- 4   1.55   FALSE    HT    WL   
24        0.869    2.57e-10   3.39   TRUE     HT    ZB   
25        0.738    8.05e- 6   1.94   FALSE    LB    CB   
26        0.726    3.73e- 4   1.50   TRUE     LB    MI   
27        0.748    0.         4.15   TRUE     MI    AW   
28        0.864    3.18e- 4   2.94   FALSE    MI    NO   
29        0.712    1.97e-11   3.38   TRUE     MI    OK   
30        0.786    0.         4.18   TRUE     MI    RT   
31        0.799    8.88e-16   3.49   TRUE     MI    ZB   
32        0.603    5.46e- 2   1.12   TRUE     RT    WL   
33        0.815    4.36e- 4   2.06   TRUE     SI    AD   
34        0.740    5.55e-16   4.01   TRUE     SI    AW   
35        0.748    4.13e- 9   3.18   TRUE     SI    BT   
36        0.724    9.94e-12   3.18   TRUE     SI    CB   
37        0.789    2.11e-15   3.93   TRUE     SI    GH   
38        0.762    7.13e- 1   0.576  TRUE     SI    HN   
39        0.836    6.99e- 4   2.81   TRUE     SI    HT   
40        0.730    6.40e- 3   1.55   TRUE     SI    LB   
41        0.853    4.30e- 3   2.80   TRUE     SI    NO   
42        0.692    1.91e-10   3.17   TRUE     SI    OK   
43        0.835    4.25e-14   3.86   TRUE     SI    PL   
44        0.780    3.82e- 9   2.54   TRUE     SI    PM   
45        0.691    1.30e- 8   2.94   TRUE     SI    RC   
46        0.774    2.22e-16   4.05   TRUE     SI    RT   
47        0.789    1.11e-15   3.51   TRUE     SI    ZB   
48        0.817    8.20e- 6   2.77   TRUE     WL    ZB   

Modelling function received variables: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Modelling function received variables: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Getting Model Summary: 
Linear mixed model fit by maximum likelihood  ['lmerMod']
Formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) +      (1 | DEST)
   Data: data_item

     AIC      BIC   logLik deviance df.resid 
  -132.0   -118.9     73.0   -146.0       41 

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-1.89659 -0.60863 -0.08118  0.72351  2.01209 

Random effects:
 Groups   Name        Variance Std.Dev.
 DEST     (Intercept) 0.001577 0.03971 
 PORT     (Intercept) 0.000000 0.00000 
 Residual             0.001845 0.04295 
Number of obs: 48, groups:  DEST, 17; PORT, 11

Fixed effects:
              Estimate Std. Error t value
(Intercept)   0.715394   0.026757  26.737
B_FON_NOECO  -0.033056   0.034093  -0.970
PRED_ENV      0.028324   0.008562   3.308
ECO_DIFFTRUE -0.024783   0.018732  -1.323

Correlation of Fixed Effects:
            (Intr) B_FON_ PRED_E
B_FON_NOECO -0.544              
PRED_ENV    -0.698  0.491       
ECO_DIFFTRU -0.391  0.107 -0.220
convergence code: 0
boundary (singular) fit: see ?isSingular

Getting Model Coefficients from Summary: 
                Estimate  Std. Error    t value
(Intercept)   0.71539351 0.026756703 26.7369825
B_FON_NOECO  -0.03305618 0.034092789 -0.9695945
PRED_ENV      0.02832402 0.008561584  3.3082689
ECO_DIFFTRUE -0.02478349 0.018732139 -1.3230462

Getting Model ANOVA: 
Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + 
full_model:     (1 | DEST)
           Df     AIC     BIC logLik deviance  Chisq Chi Df Pr(>Chisq)
null_model  6 -133.05 -121.82 72.525  -145.05                         
full_model  7 -131.97 -118.88 72.988  -145.97 0.9261      1     0.3359

Plotting Model Coefficients: 
boundary (singular) fit: see ?isSingular

°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ 

Starting new analysis, with data index DIDX "3" and formula index FIDX "3" in Summary Tables.
Using formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 02_results_euk_otu99_deep_UNIF_model_data_2020-Jan-31-14-15-28_no_ph_with_hon_info.csv. 

Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.
- Setting types.
- Input dimensions are: 64 17 .
- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST VOY_FREQ B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .
- Kept variables are: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST .
- Intermediate dimensions are: 64 6 .
- Undefined rows have been removed, assuming they were real "NA" and not "0".
- Final dimensions are: 48 6 .
# A tibble: 48 x 6
   RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT  DEST 
          <dbl>       <dbl>    <dbl> <fct>    <fct> <fct>
 1        0.708    0.         1.06   TRUE     AW    WL   
 2        0.634    6.06e- 1   1.56   TRUE     BT    AW   
 3        0.708    7.58e- 2   1.50   TRUE     BT    GH   
 4        0.800    4.83e- 2   1.52   FALSE    BT    HT   
 5        0.791    8.92e- 4   2.14   TRUE     BT    LB   
 6        0.751    6.07e- 8   3.16   FALSE    BT    MI   
 7        0.802    1.83e- 1   1.56   FALSE    BT    NO   
 8        0.732    5.48e- 1   1.57   TRUE     BT    RT   
 9        0.740    9.60e- 1   0.921  FALSE    BT    WL   
10        0.786    5.75e- 2   2.25   TRUE     BT    ZB   
11        0.831    2.28e- 2   1.29   FALSE    CB    PL   
12        0.688    2.07e- 4   0.547  FALSE    CB    RC   
13        0.671    1.23e- 2   1.07   TRUE     GH    WL   
14        0.736    1.00e-11   2.79   TRUE     HN    CB   
15        0.830    0.         2.81   TRUE     HN    HT   
16        0.781    8.75e- 6   2.11   TRUE     HT    AW   
17        0.753    2.48e- 6   2.09   TRUE     HT    GH   
18        0.892    3.08e- 4   2.53   TRUE     HT    LB   
19        0.851    3.56e- 4   2.94   FALSE    HT    MI   
20        0.635    1.00e+ 0   0.0459 FALSE    HT    NO   
21        0.824    0.         2.74   TRUE     HT    PM   
22        0.700    3.59e- 6   2.23   TRUE     HT    RT   
23        0.644    7.49e- 4   1.55   FALSE    HT    WL   
24        0.879    2.57e-10   3.39   TRUE     HT    ZB   
25        0.730    8.05e- 6   1.94   FALSE    LB    CB   
26        0.726    3.73e- 4   1.50   TRUE     LB    MI   
27        0.747    0.         4.15   TRUE     MI    AW   
28        0.870    3.18e- 4   2.94   FALSE    MI    NO   
29        0.715    1.97e-11   3.38   TRUE     MI    OK   
30        0.789    0.         4.18   TRUE     MI    RT   
31        0.802    8.88e-16   3.49   TRUE     MI    ZB   
32        0.607    5.46e- 2   1.12   TRUE     RT    WL   
33        0.815    4.36e- 4   2.06   TRUE     SI    AD   
34        0.737    5.55e-16   4.01   TRUE     SI    AW   
35        0.747    4.13e- 9   3.18   TRUE     SI    BT   
36        0.724    9.94e-12   3.18   TRUE     SI    CB   
37        0.790    2.11e-15   3.93   TRUE     SI    GH   
38        0.768    7.13e- 1   0.576  TRUE     SI    HN   
39        0.837    6.99e- 4   2.81   TRUE     SI    HT   
40        0.732    6.40e- 3   1.55   TRUE     SI    LB   
41        0.851    4.30e- 3   2.80   TRUE     SI    NO   
42        0.698    1.91e-10   3.17   TRUE     SI    OK   
43        0.836    4.25e-14   3.86   TRUE     SI    PL   
44        0.780    3.82e- 9   2.54   TRUE     SI    PM   
45        0.702    1.30e- 8   2.94   TRUE     SI    RC   
46        0.774    2.22e-16   4.05   TRUE     SI    RT   
47        0.786    1.11e-15   3.51   TRUE     SI    ZB   
48        0.821    8.20e- 6   2.77   TRUE     WL    ZB   

Modelling function received variables: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Modelling function received variables: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Getting Model Summary: 
Linear mixed model fit by maximum likelihood  ['lmerMod']
Formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) +      (1 | DEST)
   Data: data_item

     AIC      BIC   logLik deviance df.resid 
  -130.3   -117.2     72.2   -144.3       41 

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-1.84400 -0.60695 -0.09389  0.62813  2.04404 

Random effects:
 Groups   Name        Variance Std.Dev.
 DEST     (Intercept) 0.001445 0.03802 
 PORT     (Intercept) 0.000000 0.00000 
 Residual             0.001982 0.04452 
Number of obs: 48, groups:  DEST, 17; PORT, 11

Fixed effects:
             Estimate Std. Error t value
(Intercept)   0.72054    0.02724  26.451
B_FON_NOECO  -0.03048    0.03512  -0.868
PRED_ENV      0.02716    0.00881   3.083
ECO_DIFFTRUE -0.02450    0.01924  -1.273

Correlation of Fixed Effects:
            (Intr) B_FON_ PRED_E
B_FON_NOECO -0.549              
PRED_ENV    -0.704  0.492       
ECO_DIFFTRU -0.390  0.103 -0.224
convergence code: 0
boundary (singular) fit: see ?isSingular

Getting Model Coefficients from Summary: 
                Estimate  Std. Error    t value
(Intercept)   0.72054278 0.027240762 26.4509043
B_FON_NOECO  -0.03048395 0.035115769 -0.8680985
PRED_ENV      0.02716230 0.008809728  3.0832167
ECO_DIFFTRUE -0.02450225 0.019242120 -1.2733656

Getting Model ANOVA: 
Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + 
full_model:     (1 | DEST)
           Df     AIC     BIC logLik deviance  Chisq Chi Df Pr(>Chisq)
null_model  6 -131.56 -120.33 71.780  -143.56                         
full_model  7 -130.31 -117.21 72.153  -144.31 0.7453      1      0.388

Plotting Model Coefficients: 
boundary (singular) fit: see ?isSingular

°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ 

Starting new analysis, with data index DIDX "4" and formula index FIDX "3" in Summary Tables.
Using formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 02_results_euk_otu99_deep_UNIF_model_data_2020-Jan-31-14-15-28_with_hon_info.csv. 

Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.
- Setting types.
- Input dimensions are: 69 17 .
- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST VOY_FREQ B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .
- Kept variables are: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST .
- Intermediate dimensions are: 69 6 .
- Undefined rows have been removed, assuming they were real "NA" and not "0".
- Final dimensions are: 48 6 .
# A tibble: 48 x 6
   RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT  DEST 
          <dbl>       <dbl>    <dbl> <fct>    <fct> <fct>
 1        0.708    0.         1.06   TRUE     AW    WL   
 2        0.634    6.06e- 1   1.56   TRUE     BT    AW   
 3        0.708    7.58e- 2   1.50   TRUE     BT    GH   
 4        0.800    4.83e- 2   1.52   FALSE    BT    HT   
 5        0.791    8.92e- 4   2.14   TRUE     BT    LB   
 6        0.751    6.07e- 8   3.16   FALSE    BT    MI   
 7        0.802    1.83e- 1   1.56   FALSE    BT    NO   
 8        0.732    5.48e- 1   1.57   TRUE     BT    RT   
 9        0.740    9.60e- 1   0.921  FALSE    BT    WL   
10        0.786    5.75e- 2   2.25   TRUE     BT    ZB   
11        0.831    2.28e- 2   1.29   FALSE    CB    PL   
12        0.688    2.07e- 4   0.547  FALSE    CB    RC   
13        0.671    1.23e- 2   1.07   TRUE     GH    WL   
14        0.736    1.00e-11   2.79   TRUE     HN    CB   
15        0.830    0.         2.81   TRUE     HN    HT   
16        0.781    8.75e- 6   2.11   TRUE     HT    AW   
17        0.753    2.48e- 6   2.09   TRUE     HT    GH   
18        0.892    3.08e- 4   2.53   TRUE     HT    LB   
19        0.851    3.56e- 4   2.94   FALSE    HT    MI   
20        0.635    1.00e+ 0   0.0459 FALSE    HT    NO   
21        0.824    0.         2.74   TRUE     HT    PM   
22        0.700    3.59e- 6   2.23   TRUE     HT    RT   
23        0.644    7.49e- 4   1.55   FALSE    HT    WL   
24        0.879    2.57e-10   3.39   TRUE     HT    ZB   
25        0.730    8.05e- 6   1.94   FALSE    LB    CB   
26        0.726    3.73e- 4   1.50   TRUE     LB    MI   
27        0.747    0.         4.15   TRUE     MI    AW   
28        0.870    3.18e- 4   2.94   FALSE    MI    NO   
29        0.715    1.97e-11   3.38   TRUE     MI    OK   
30        0.789    0.         4.18   TRUE     MI    RT   
31        0.802    8.88e-16   3.49   TRUE     MI    ZB   
32        0.607    5.46e- 2   1.12   TRUE     RT    WL   
33        0.815    4.36e- 4   2.06   TRUE     SI    AD   
34        0.737    5.55e-16   4.01   TRUE     SI    AW   
35        0.747    4.13e- 9   3.18   TRUE     SI    BT   
36        0.724    9.94e-12   3.18   TRUE     SI    CB   
37        0.790    2.11e-15   3.93   TRUE     SI    GH   
38        0.768    7.13e- 1   0.576  TRUE     SI    HN   
39        0.837    6.99e- 4   2.81   TRUE     SI    HT   
40        0.732    6.40e- 3   1.55   TRUE     SI    LB   
41        0.851    4.30e- 3   2.80   TRUE     SI    NO   
42        0.698    1.91e-10   3.17   TRUE     SI    OK   
43        0.836    4.25e-14   3.86   TRUE     SI    PL   
44        0.780    3.82e- 9   2.54   TRUE     SI    PM   
45        0.702    1.30e- 8   2.94   TRUE     SI    RC   
46        0.774    2.22e-16   4.05   TRUE     SI    RT   
47        0.786    1.11e-15   3.51   TRUE     SI    ZB   
48        0.821    8.20e- 6   2.77   TRUE     WL    ZB   

Modelling function received variables: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Modelling function received variables: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Getting Model Summary: 
Linear mixed model fit by maximum likelihood  ['lmerMod']
Formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) +      (1 | DEST)
   Data: data_item

     AIC      BIC   logLik deviance df.resid 
  -130.3   -117.2     72.2   -144.3       41 

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-1.84400 -0.60695 -0.09389  0.62813  2.04404 

Random effects:
 Groups   Name        Variance Std.Dev.
 DEST     (Intercept) 0.001445 0.03802 
 PORT     (Intercept) 0.000000 0.00000 
 Residual             0.001982 0.04452 
Number of obs: 48, groups:  DEST, 17; PORT, 11

Fixed effects:
             Estimate Std. Error t value
(Intercept)   0.72054    0.02724  26.451
B_FON_NOECO  -0.03048    0.03512  -0.868
PRED_ENV      0.02716    0.00881   3.083
ECO_DIFFTRUE -0.02450    0.01924  -1.273

Correlation of Fixed Effects:
            (Intr) B_FON_ PRED_E
B_FON_NOECO -0.549              
PRED_ENV    -0.704  0.492       
ECO_DIFFTRU -0.390  0.103 -0.224
convergence code: 0
boundary (singular) fit: see ?isSingular

Getting Model Coefficients from Summary: 
                Estimate  Std. Error    t value
(Intercept)   0.72054278 0.027240762 26.4509043
B_FON_NOECO  -0.03048395 0.035115769 -0.8680985
PRED_ENV      0.02716230 0.008809728  3.0832167
ECO_DIFFTRUE -0.02450225 0.019242120 -1.2733656

Getting Model ANOVA: 
Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + 
full_model:     (1 | DEST)
           Df     AIC     BIC logLik deviance  Chisq Chi Df Pr(>Chisq)
null_model  6 -131.56 -120.33 71.780  -143.56                         
full_model  7 -130.31 -117.21 72.153  -144.31 0.7453      1      0.388

Plotting Model Coefficients: 
boundary (singular) fit: see ?isSingular

°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ 

Starting new analysis, with data index DIDX "5" and formula index FIDX "3" in Summary Tables.
Using formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 03_results_euk_asv00_deep_JAQU_model_data_2020-Jan-31-14-15-41_no_ph_with_hon_info.csv. 

Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.
- Setting types.
- Input dimensions are: 64 17 .
- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST VOY_FREQ B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .
- Kept variables are: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST .
- Intermediate dimensions are: 64 6 .
- Undefined rows have been removed, assuming they were real "NA" and not "0".
- Final dimensions are: 48 6 .
# A tibble: 48 x 6
   RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT  DEST 
          <dbl>       <dbl>    <dbl> <fct>    <fct> <fct>
 1        0.950    0.         1.06   TRUE     AW    WL   
 2        0.902    6.06e- 1   1.56   TRUE     BT    AW   
 3        0.942    7.58e- 2   1.50   TRUE     BT    GH   
 4        0.989    4.83e- 2   1.52   FALSE    BT    HT   
 5        0.964    8.92e- 4   2.14   TRUE     BT    LB   
 6        0.974    6.07e- 8   3.16   FALSE    BT    MI   
 7        0.985    1.83e- 1   1.56   FALSE    BT    NO   
 8        0.955    5.48e- 1   1.57   TRUE     BT    RT   
 9        0.958    9.60e- 1   0.921  FALSE    BT    WL   
10        0.955    5.75e- 2   2.25   TRUE     BT    ZB   
11        1        2.28e- 2   1.29   FALSE    CB    PL   
12        0.908    2.07e- 4   0.547  FALSE    CB    RC   
13        0.913    1.23e- 2   1.07   TRUE     GH    WL   
14        0.980    1.00e-11   2.79   TRUE     HN    CB   
15        0.993    0.         2.81   TRUE     HN    HT   
16        0.988    8.75e- 6   2.11   TRUE     HT    AW   
17        0.969    2.48e- 6   2.09   TRUE     HT    GH   
18        1.00     3.08e- 4   2.53   TRUE     HT    LB   
19        0.994    3.56e- 4   2.94   FALSE    HT    MI   
20        0.892    1.00e+ 0   0.0459 FALSE    HT    NO   
21        0.997    0.         2.74   TRUE     HT    PM   
22        0.948    3.59e- 6   2.23   TRUE     HT    RT   
23        0.906    7.49e- 4   1.55   FALSE    HT    WL   
24        0.999    2.57e-10   3.39   TRUE     HT    ZB   
25        0.940    8.05e- 6   1.94   FALSE    LB    CB   
26        0.943    3.73e- 4   1.50   TRUE     LB    MI   
27        0.988    0.         4.15   TRUE     MI    AW   
28        0.998    3.18e- 4   2.94   FALSE    MI    NO   
29        0.963    1.97e-11   3.38   TRUE     MI    OK   
30        0.991    0.         4.18   TRUE     MI    RT   
31        0.988    8.88e-16   3.49   TRUE     MI    ZB   
32        0.894    5.46e- 2   1.12   TRUE     RT    WL   
33        0.971    4.36e- 4   2.06   TRUE     SI    AD   
34        0.985    5.55e-16   4.01   TRUE     SI    AW   
35        0.973    4.13e- 9   3.18   TRUE     SI    BT   
36        0.981    9.94e-12   3.18   TRUE     SI    CB   
37        0.995    2.11e-15   3.93   TRUE     SI    GH   
38        0.959    7.13e- 1   0.576  TRUE     SI    HN   
39        0.997    6.99e- 4   2.81   TRUE     SI    HT   
40        0.967    6.40e- 3   1.55   TRUE     SI    LB   
41        0.997    4.30e- 3   2.80   TRUE     SI    NO   
42        0.958    1.91e-10   3.17   TRUE     SI    OK   
43        0.998    4.25e-14   3.86   TRUE     SI    PL   
44        0.996    3.82e- 9   2.54   TRUE     SI    PM   
45        0.965    1.30e- 8   2.94   TRUE     SI    RC   
46        0.992    2.22e-16   4.05   TRUE     SI    RT   
47        0.984    1.11e-15   3.51   TRUE     SI    ZB   
48        0.996    8.20e- 6   2.77   TRUE     WL    ZB   

Modelling function received variables: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Modelling function received variables: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Getting Model Summary: 
Linear mixed model fit by maximum likelihood  ['lmerMod']
Formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) +      (1 | DEST)
   Data: data_item

     AIC      BIC   logLik deviance df.resid 
  -216.2   -203.1    115.1   -230.2       41 

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.0393 -0.4659 -0.1089  0.5976  1.9272 

Random effects:
 Groups   Name        Variance  Std.Dev.
 DEST     (Intercept) 9.362e-05 0.009676
 PORT     (Intercept) 0.000e+00 0.000000
 Residual             4.079e-04 0.020197
Number of obs: 48, groups:  DEST, 17; PORT, 11

Fixed effects:
              Estimate Std. Error t value
(Intercept)   0.929066   0.011060  84.004
B_FON_NOECO  -0.008958   0.015226  -0.588
PRED_ENV      0.019068   0.003771   5.057
ECO_DIFFTRUE -0.006355   0.008160  -0.779

Correlation of Fixed Effects:
            (Intr) B_FON_ PRED_E
B_FON_NOECO -0.570              
PRED_ENV    -0.731  0.495       
ECO_DIFFTRU -0.375  0.081 -0.253
convergence code: 0
boundary (singular) fit: see ?isSingular

Getting Model Coefficients from Summary: 
                 Estimate  Std. Error    t value
(Intercept)   0.929065909 0.011059769 84.0040949
B_FON_NOECO  -0.008957681 0.015225644 -0.5883285
PRED_ENV      0.019067655 0.003770907  5.0565167
ECO_DIFFTRUE -0.006354684 0.008160338 -0.7787280

Getting Model ANOVA: 
Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + 
full_model:     (1 | DEST)
           Df     AIC     BIC logLik deviance  Chisq Chi Df Pr(>Chisq)
null_model  6 -217.82 -206.59 114.91  -229.82                         
full_model  7 -216.16 -203.07 115.08  -230.16 0.3449      1      0.557

Plotting Model Coefficients: 
boundary (singular) fit: see ?isSingular

°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ 

Starting new analysis, with data index DIDX "6" and formula index FIDX "3" in Summary Tables.
Using formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 03_results_euk_asv00_deep_JAQU_model_data_2020-Jan-31-14-15-41_with_hon_info.csv. 

Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.
- Setting types.
- Input dimensions are: 69 17 .
- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST VOY_FREQ B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .
- Kept variables are: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST .
- Intermediate dimensions are: 69 6 .
- Undefined rows have been removed, assuming they were real "NA" and not "0".
- Final dimensions are: 48 6 .
# A tibble: 48 x 6
   RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT  DEST 
          <dbl>       <dbl>    <dbl> <fct>    <fct> <fct>
 1        0.950    0.         1.06   TRUE     AW    WL   
 2        0.902    6.06e- 1   1.56   TRUE     BT    AW   
 3        0.942    7.58e- 2   1.50   TRUE     BT    GH   
 4        0.989    4.83e- 2   1.52   FALSE    BT    HT   
 5        0.964    8.92e- 4   2.14   TRUE     BT    LB   
 6        0.974    6.07e- 8   3.16   FALSE    BT    MI   
 7        0.985    1.83e- 1   1.56   FALSE    BT    NO   
 8        0.955    5.48e- 1   1.57   TRUE     BT    RT   
 9        0.958    9.60e- 1   0.921  FALSE    BT    WL   
10        0.955    5.75e- 2   2.25   TRUE     BT    ZB   
11        1        2.28e- 2   1.29   FALSE    CB    PL   
12        0.908    2.07e- 4   0.547  FALSE    CB    RC   
13        0.913    1.23e- 2   1.07   TRUE     GH    WL   
14        0.980    1.00e-11   2.79   TRUE     HN    CB   
15        0.993    0.         2.81   TRUE     HN    HT   
16        0.988    8.75e- 6   2.11   TRUE     HT    AW   
17        0.969    2.48e- 6   2.09   TRUE     HT    GH   
18        1.00     3.08e- 4   2.53   TRUE     HT    LB   
19        0.994    3.56e- 4   2.94   FALSE    HT    MI   
20        0.892    1.00e+ 0   0.0459 FALSE    HT    NO   
21        0.997    0.         2.74   TRUE     HT    PM   
22        0.948    3.59e- 6   2.23   TRUE     HT    RT   
23        0.906    7.49e- 4   1.55   FALSE    HT    WL   
24        0.999    2.57e-10   3.39   TRUE     HT    ZB   
25        0.940    8.05e- 6   1.94   FALSE    LB    CB   
26        0.943    3.73e- 4   1.50   TRUE     LB    MI   
27        0.988    0.         4.15   TRUE     MI    AW   
28        0.998    3.18e- 4   2.94   FALSE    MI    NO   
29        0.963    1.97e-11   3.38   TRUE     MI    OK   
30        0.991    0.         4.18   TRUE     MI    RT   
31        0.988    8.88e-16   3.49   TRUE     MI    ZB   
32        0.894    5.46e- 2   1.12   TRUE     RT    WL   
33        0.971    4.36e- 4   2.06   TRUE     SI    AD   
34        0.985    5.55e-16   4.01   TRUE     SI    AW   
35        0.973    4.13e- 9   3.18   TRUE     SI    BT   
36        0.981    9.94e-12   3.18   TRUE     SI    CB   
37        0.995    2.11e-15   3.93   TRUE     SI    GH   
38        0.959    7.13e- 1   0.576  TRUE     SI    HN   
39        0.997    6.99e- 4   2.81   TRUE     SI    HT   
40        0.967    6.40e- 3   1.55   TRUE     SI    LB   
41        0.997    4.30e- 3   2.80   TRUE     SI    NO   
42        0.958    1.91e-10   3.17   TRUE     SI    OK   
43        0.998    4.25e-14   3.86   TRUE     SI    PL   
44        0.996    3.82e- 9   2.54   TRUE     SI    PM   
45        0.965    1.30e- 8   2.94   TRUE     SI    RC   
46        0.992    2.22e-16   4.05   TRUE     SI    RT   
47        0.984    1.11e-15   3.51   TRUE     SI    ZB   
48        0.996    8.20e- 6   2.77   TRUE     WL    ZB   

Modelling function received variables: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Modelling function received variables: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Getting Model Summary: 
Linear mixed model fit by maximum likelihood  ['lmerMod']
Formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) +      (1 | DEST)
   Data: data_item

     AIC      BIC   logLik deviance df.resid 
  -216.2   -203.1    115.1   -230.2       41 

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.0393 -0.4659 -0.1089  0.5976  1.9272 

Random effects:
 Groups   Name        Variance  Std.Dev.
 DEST     (Intercept) 9.362e-05 0.009676
 PORT     (Intercept) 0.000e+00 0.000000
 Residual             4.079e-04 0.020197
Number of obs: 48, groups:  DEST, 17; PORT, 11

Fixed effects:
              Estimate Std. Error t value
(Intercept)   0.929066   0.011060  84.004
B_FON_NOECO  -0.008958   0.015226  -0.588
PRED_ENV      0.019068   0.003771   5.057
ECO_DIFFTRUE -0.006355   0.008160  -0.779

Correlation of Fixed Effects:
            (Intr) B_FON_ PRED_E
B_FON_NOECO -0.570              
PRED_ENV    -0.731  0.495       
ECO_DIFFTRU -0.375  0.081 -0.253
convergence code: 0
boundary (singular) fit: see ?isSingular

Getting Model Coefficients from Summary: 
                 Estimate  Std. Error    t value
(Intercept)   0.929065909 0.011059769 84.0040949
B_FON_NOECO  -0.008957681 0.015225644 -0.5883285
PRED_ENV      0.019067655 0.003770907  5.0565167
ECO_DIFFTRUE -0.006354684 0.008160338 -0.7787280

Getting Model ANOVA: 
Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + 
full_model:     (1 | DEST)
           Df     AIC     BIC logLik deviance  Chisq Chi Df Pr(>Chisq)
null_model  6 -217.82 -206.59 114.91  -229.82                         
full_model  7 -216.16 -203.07 115.08  -230.16 0.3449      1      0.557

Plotting Model Coefficients: 
boundary (singular) fit: see ?isSingular

°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ 

Starting new analysis, with data index DIDX "7" and formula index FIDX "3" in Summary Tables.
Using formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 04_results_euk_otu99_deep_JAQU_model_data_2020-Jan-31-14-15-52_no_ph_with_hon_info.csv. 

Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.
- Setting types.
- Input dimensions are: 64 17 .
- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST VOY_FREQ B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .
- Kept variables are: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST .
- Intermediate dimensions are: 64 6 .
- Undefined rows have been removed, assuming they were real "NA" and not "0".
- Final dimensions are: 48 6 .
# A tibble: 48 x 6
   RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT  DEST 
          <dbl>       <dbl>    <dbl> <fct>    <fct> <fct>
 1        0.919    0.         1.06   TRUE     AW    WL   
 2        0.848    6.06e- 1   1.56   TRUE     BT    AW   
 3        0.907    7.58e- 2   1.50   TRUE     BT    GH   
 4        0.980    4.83e- 2   1.52   FALSE    BT    HT   
 5        0.938    8.92e- 4   2.14   TRUE     BT    LB   
 6        0.934    6.07e- 8   3.16   FALSE    BT    MI   
 7        0.971    1.83e- 1   1.56   FALSE    BT    NO   
 8        0.928    5.48e- 1   1.57   TRUE     BT    RT   
 9        0.935    9.60e- 1   0.921  FALSE    BT    WL   
10        0.932    5.75e- 2   2.25   TRUE     BT    ZB   
11        0.998    2.28e- 2   1.29   FALSE    CB    PL   
12        0.863    2.07e- 4   0.547  FALSE    CB    RC   
13        0.879    1.23e- 2   1.07   TRUE     GH    WL   
14        0.948    1.00e-11   2.79   TRUE     HN    CB   
15        0.988    0.         2.81   TRUE     HN    HT   
16        0.981    8.75e- 6   2.11   TRUE     HT    AW   
17        0.951    2.48e- 6   2.09   TRUE     HT    GH   
18        0.998    3.08e- 4   2.53   TRUE     HT    LB   
19        0.987    3.56e- 4   2.94   FALSE    HT    MI   
20        0.848    1.00e+ 0   0.0459 FALSE    HT    NO   
21        0.995    0.         2.74   TRUE     HT    PM   
22        0.921    3.59e- 6   2.23   TRUE     HT    RT   
23        0.866    7.49e- 4   1.55   FALSE    HT    WL   
24        0.997    2.57e-10   3.39   TRUE     HT    ZB   
25        0.902    8.05e- 6   1.94   FALSE    LB    CB   
26        0.908    3.73e- 4   1.50   TRUE     LB    MI   
27        0.967    0.         4.15   TRUE     MI    AW   
28        0.990    3.18e- 4   2.94   FALSE    MI    NO   
29        0.928    1.97e-11   3.38   TRUE     MI    OK   
30        0.977    0.         4.18   TRUE     MI    RT   
31        0.966    8.88e-16   3.49   TRUE     MI    ZB   
32        0.840    5.46e- 2   1.12   TRUE     RT    WL   
33        0.946    4.36e- 4   2.06   TRUE     SI    AD   
34        0.960    5.55e-16   4.01   TRUE     SI    AW   
35        0.947    4.13e- 9   3.18   TRUE     SI    BT   
36        0.950    9.94e-12   3.18   TRUE     SI    CB   
37        0.986    2.11e-15   3.93   TRUE     SI    GH   
38        0.935    7.13e- 1   0.576  TRUE     SI    HN   
39        0.993    6.99e- 4   2.81   TRUE     SI    HT   
40        0.932    6.40e- 3   1.55   TRUE     SI    LB   
41        0.994    4.30e- 3   2.80   TRUE     SI    NO   
42        0.922    1.91e-10   3.17   TRUE     SI    OK   
43        0.996    4.25e-14   3.86   TRUE     SI    PL   
44        0.983    3.82e- 9   2.54   TRUE     SI    PM   
45        0.930    1.30e- 8   2.94   TRUE     SI    RC   
46        0.976    2.22e-16   4.05   TRUE     SI    RT   
47        0.952    1.11e-15   3.51   TRUE     SI    ZB   
48        0.987    8.20e- 6   2.77   TRUE     WL    ZB   

Modelling function received variables: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Modelling function received variables: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Getting Model Summary: 
Linear mixed model fit by maximum likelihood  ['lmerMod']
Formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) +      (1 | DEST)
   Data: data_item

     AIC      BIC   logLik deviance df.resid 
  -175.9   -162.8     94.9   -189.9       41 

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-1.9400 -0.5252 -0.1045  0.7998  1.7730 

Random effects:
 Groups   Name        Variance  Std.Dev.
 DEST     (Intercept) 0.0002932 0.01712 
 PORT     (Intercept) 0.0000000 0.00000 
 Residual             0.0008959 0.02993 
Number of obs: 48, groups:  DEST, 17; PORT, 11

Fixed effects:
              Estimate Std. Error t value
(Intercept)   0.897203   0.016894  53.107
B_FON_NOECO  -0.014758   0.022861  -0.646
PRED_ENV      0.024307   0.005689   4.272
ECO_DIFFTRUE -0.009903   0.012336  -0.803

Correlation of Fixed Effects:
            (Intr) B_FON_ PRED_E
B_FON_NOECO -0.565              
PRED_ENV    -0.726  0.495       
ECO_DIFFTRU -0.381  0.087 -0.244
convergence code: 0
boundary (singular) fit: see ?isSingular

Getting Model Coefficients from Summary: 
                 Estimate Std. Error    t value
(Intercept)   0.897203218 0.01689411 53.1074644
B_FON_NOECO  -0.014757655 0.02286148 -0.6455249
PRED_ENV      0.024306591 0.00568914  4.2724546
ECO_DIFFTRUE -0.009902955 0.01233641 -0.8027422

Getting Model ANOVA: 
Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + 
full_model:     (1 | DEST)
           Df     AIC     BIC logLik deviance  Chisq Chi Df Pr(>Chisq)
null_model  6 -177.49 -166.26 94.743  -189.49                         
full_model  7 -175.90 -162.80 94.950  -189.90 0.4134      1     0.5203

Plotting Model Coefficients: 
boundary (singular) fit: see ?isSingular

°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ 

Starting new analysis, with data index DIDX "8" and formula index FIDX "3" in Summary Tables.
Using formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 04_results_euk_otu99_deep_JAQU_model_data_2020-Jan-31-14-15-52_with_hon_info.csv. 

Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.
- Setting types.
- Input dimensions are: 69 17 .
- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST VOY_FREQ B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .
- Kept variables are: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST .
- Intermediate dimensions are: 69 6 .
- Undefined rows have been removed, assuming they were real "NA" and not "0".
- Final dimensions are: 48 6 .
# A tibble: 48 x 6
   RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT  DEST 
          <dbl>       <dbl>    <dbl> <fct>    <fct> <fct>
 1        0.919    0.         1.06   TRUE     AW    WL   
 2        0.848    6.06e- 1   1.56   TRUE     BT    AW   
 3        0.907    7.58e- 2   1.50   TRUE     BT    GH   
 4        0.980    4.83e- 2   1.52   FALSE    BT    HT   
 5        0.938    8.92e- 4   2.14   TRUE     BT    LB   
 6        0.934    6.07e- 8   3.16   FALSE    BT    MI   
 7        0.971    1.83e- 1   1.56   FALSE    BT    NO   
 8        0.928    5.48e- 1   1.57   TRUE     BT    RT   
 9        0.935    9.60e- 1   0.921  FALSE    BT    WL   
10        0.932    5.75e- 2   2.25   TRUE     BT    ZB   
11        0.998    2.28e- 2   1.29   FALSE    CB    PL   
12        0.863    2.07e- 4   0.547  FALSE    CB    RC   
13        0.879    1.23e- 2   1.07   TRUE     GH    WL   
14        0.948    1.00e-11   2.79   TRUE     HN    CB   
15        0.988    0.         2.81   TRUE     HN    HT   
16        0.981    8.75e- 6   2.11   TRUE     HT    AW   
17        0.951    2.48e- 6   2.09   TRUE     HT    GH   
18        0.998    3.08e- 4   2.53   TRUE     HT    LB   
19        0.987    3.56e- 4   2.94   FALSE    HT    MI   
20        0.848    1.00e+ 0   0.0459 FALSE    HT    NO   
21        0.995    0.         2.74   TRUE     HT    PM   
22        0.921    3.59e- 6   2.23   TRUE     HT    RT   
23        0.866    7.49e- 4   1.55   FALSE    HT    WL   
24        0.997    2.57e-10   3.39   TRUE     HT    ZB   
25        0.902    8.05e- 6   1.94   FALSE    LB    CB   
26        0.908    3.73e- 4   1.50   TRUE     LB    MI   
27        0.967    0.         4.15   TRUE     MI    AW   
28        0.990    3.18e- 4   2.94   FALSE    MI    NO   
29        0.928    1.97e-11   3.38   TRUE     MI    OK   
30        0.977    0.         4.18   TRUE     MI    RT   
31        0.966    8.88e-16   3.49   TRUE     MI    ZB   
32        0.840    5.46e- 2   1.12   TRUE     RT    WL   
33        0.946    4.36e- 4   2.06   TRUE     SI    AD   
34        0.960    5.55e-16   4.01   TRUE     SI    AW   
35        0.947    4.13e- 9   3.18   TRUE     SI    BT   
36        0.950    9.94e-12   3.18   TRUE     SI    CB   
37        0.986    2.11e-15   3.93   TRUE     SI    GH   
38        0.935    7.13e- 1   0.576  TRUE     SI    HN   
39        0.993    6.99e- 4   2.81   TRUE     SI    HT   
40        0.932    6.40e- 3   1.55   TRUE     SI    LB   
41        0.994    4.30e- 3   2.80   TRUE     SI    NO   
42        0.922    1.91e-10   3.17   TRUE     SI    OK   
43        0.996    4.25e-14   3.86   TRUE     SI    PL   
44        0.983    3.82e- 9   2.54   TRUE     SI    PM   
45        0.930    1.30e- 8   2.94   TRUE     SI    RC   
46        0.976    2.22e-16   4.05   TRUE     SI    RT   
47        0.952    1.11e-15   3.51   TRUE     SI    ZB   
48        0.987    8.20e- 6   2.77   TRUE     WL    ZB   

Modelling function received variables: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Modelling function received variables: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Getting Model Summary: 
Linear mixed model fit by maximum likelihood  ['lmerMod']
Formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) +      (1 | DEST)
   Data: data_item

     AIC      BIC   logLik deviance df.resid 
  -175.9   -162.8     94.9   -189.9       41 

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-1.9400 -0.5252 -0.1045  0.7998  1.7730 

Random effects:
 Groups   Name        Variance  Std.Dev.
 DEST     (Intercept) 0.0002932 0.01712 
 PORT     (Intercept) 0.0000000 0.00000 
 Residual             0.0008959 0.02993 
Number of obs: 48, groups:  DEST, 17; PORT, 11

Fixed effects:
              Estimate Std. Error t value
(Intercept)   0.897203   0.016894  53.107
B_FON_NOECO  -0.014758   0.022861  -0.646
PRED_ENV      0.024307   0.005689   4.272
ECO_DIFFTRUE -0.009903   0.012336  -0.803

Correlation of Fixed Effects:
            (Intr) B_FON_ PRED_E
B_FON_NOECO -0.565              
PRED_ENV    -0.726  0.495       
ECO_DIFFTRU -0.381  0.087 -0.244
convergence code: 0
boundary (singular) fit: see ?isSingular

Getting Model Coefficients from Summary: 
                 Estimate Std. Error    t value
(Intercept)   0.897203218 0.01689411 53.1074644
B_FON_NOECO  -0.014757655 0.02286148 -0.6455249
PRED_ENV      0.024306591 0.00568914  4.2724546
ECO_DIFFTRUE -0.009902955 0.01233641 -0.8027422

Getting Model ANOVA: 
Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + 
full_model:     (1 | DEST)
           Df     AIC     BIC logLik deviance  Chisq Chi Df Pr(>Chisq)
null_model  6 -177.49 -166.26 94.743  -189.49                         
full_model  7 -175.90 -162.80 94.950  -189.90 0.4134      1     0.5203

Plotting Model Coefficients: 
boundary (singular) fit: see ?isSingular

°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ 

Starting new analysis, with data index DIDX "9" and formula index FIDX "3" in Summary Tables.
Using formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 05_results_euk_asv00_shal_UNIF_model_data_2020-Jan-31-14-16-03_no_ph_with_hon_info.csv. 

Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.
- Setting types.
- Input dimensions are: 64 17 .
- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST VOY_FREQ B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .
- Kept variables are: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST .
- Intermediate dimensions are: 64 6 .
- Undefined rows have been removed, assuming they were real "NA" and not "0".
- Final dimensions are: 48 6 .
# A tibble: 48 x 6
   RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT  DEST 
          <dbl>       <dbl>    <dbl> <fct>    <fct> <fct>
 1        0.702    0.         1.06   TRUE     AW    WL   
 2        0.634    6.06e- 1   1.56   TRUE     BT    AW   
 3        0.713    7.58e- 2   1.50   TRUE     BT    GH   
 4        0.800    4.83e- 2   1.52   FALSE    BT    HT   
 5        0.782    8.92e- 4   2.14   TRUE     BT    LB   
 6        0.760    6.07e- 8   3.16   FALSE    BT    MI   
 7        0.801    1.83e- 1   1.56   FALSE    BT    NO   
 8        0.728    5.48e- 1   1.57   TRUE     BT    RT   
 9        0.728    9.60e- 1   0.921  FALSE    BT    WL   
10        0.764    5.75e- 2   2.25   TRUE     BT    ZB   
11        0.836    2.28e- 2   1.29   FALSE    CB    PL   
12        0.683    2.07e- 4   0.547  FALSE    CB    RC   
13        0.673    1.23e- 2   1.07   TRUE     GH    WL   
14        0.741    1.00e-11   2.79   TRUE     HN    CB   
15        0.840    0.         2.81   TRUE     HN    HT   
16        0.779    8.75e- 6   2.11   TRUE     HT    AW   
17        0.765    2.48e- 6   2.09   TRUE     HT    GH   
18        0.889    3.08e- 4   2.53   TRUE     HT    LB   
19        0.849    3.56e- 4   2.94   FALSE    HT    MI   
20        0.632    1.00e+ 0   0.0459 FALSE    HT    NO   
21        0.838    0.         2.74   TRUE     HT    PM   
22        0.701    3.59e- 6   2.23   TRUE     HT    RT   
23        0.653    7.49e- 4   1.55   FALSE    HT    WL   
24        0.874    2.57e-10   3.39   TRUE     HT    ZB   
25        0.735    8.05e- 6   1.94   FALSE    LB    CB   
26        0.721    3.73e- 4   1.50   TRUE     LB    MI   
27        0.745    0.         4.15   TRUE     MI    AW   
28        0.869    3.18e- 4   2.94   FALSE    MI    NO   
29        0.719    1.97e-11   3.38   TRUE     MI    OK   
30        0.785    0.         4.18   TRUE     MI    RT   
31        0.790    8.88e-16   3.49   TRUE     MI    ZB   
32        0.603    5.46e- 2   1.12   TRUE     RT    WL   
33        0.796    4.36e- 4   2.06   TRUE     SI    AD   
34        0.741    5.55e-16   4.01   TRUE     SI    AW   
35        0.743    4.13e- 9   3.18   TRUE     SI    BT   
36        0.737    9.94e-12   3.18   TRUE     SI    CB   
37        0.797    2.11e-15   3.93   TRUE     SI    GH   
38        0.761    7.13e- 1   0.576  TRUE     SI    HN   
39        0.842    6.99e- 4   2.81   TRUE     SI    HT   
40        0.727    6.40e- 3   1.55   TRUE     SI    LB   
41        0.858    4.30e- 3   2.80   TRUE     SI    NO   
42        0.706    1.91e-10   3.17   TRUE     SI    OK   
43        0.841    4.25e-14   3.86   TRUE     SI    PL   
44        0.781    3.82e- 9   2.54   TRUE     SI    PM   
45        0.699    1.30e- 8   2.94   TRUE     SI    RC   
46        0.777    2.22e-16   4.05   TRUE     SI    RT   
47        0.777    1.11e-15   3.51   TRUE     SI    ZB   
48        0.813    8.20e- 6   2.77   TRUE     WL    ZB   

Modelling function received variables: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Modelling function received variables: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .

Getting Model Summary: 
Linear mixed model fit by maximum likelihood  ['lmerMod']
Formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) +      (1 | DEST)
   Data: data_item

     AIC      BIC   logLik deviance df.resid 
  -132.2   -119.1     73.1   -146.2       41 

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-1.7977 -0.6370 -0.1495  0.6307  2.1004 

Random effects:
 Groups   Name        Variance  Std.Dev. 
 DEST     (Intercept) 1.370e-03 3.701e-02
 PORT     (Intercept) 1.251e-12 1.118e-06
 Residual             1.913e-03 4.374e-02
Number of obs: 48, groups:  DEST, 17; PORT, 11

Fixed effects:
              Estimate Std. Error t value
(Intercept)   0.718385   0.026708  26.898
B_FON_NOECO  -0.036111   0.034473  -1.048
PRED_ENV      0.028609   0.008647   3.308
ECO_DIFFTRUE -0.026066   0.018884  -1.380

Correlation of Fixed Effects:
            (Intr) B_FON_ PRED_E
B_FON_NOECO -0.549              
PRED_ENV    -0.705  0.492       
ECO_DIFFTRU -0.390  0.103 -0.224
convergence code: 0
boundary (singular) fit: see ?isSingular

Getting Model Coefficients from Summary: 
                Estimate  Std. Error   t value
(Intercept)   0.71838513 0.026708008 26.897743
B_FON_NOECO  -0.03611096 0.034473170 -1.047509
PRED_ENV      0.02860925 0.008647411  3.308418
ECO_DIFFTRUE -0.02606615 0.018883943 -1.380334

Getting Model ANOVA: 
Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + 
full_model:     (1 | DEST)
           Df     AIC     BIC logLik deviance  Chisq Chi Df Pr(>Chisq)
null_model  6 -133.12 -121.90 72.563  -145.12                         
full_model  7 -132.21 -119.11 73.103  -146.21 1.0802      1     0.2987

Plotting Model Coefficients: 
boundary (singular) fit: see ?isSingular

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Starting new analysis, with data index DIDX "10" and formula index FIDX "3" in Summary Tables.
Using formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 05_results_euk_asv00_shal_UNIF_model_data_2020-Jan-31-14-16-03_with_hon_info.csv. 

Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.
- Setting types.
- Input dimensions are: 69 17 .
- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST VOY_FREQ B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .
- Kept variables are: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST .
- Intermediate dimensions are: 69 6 .
- Undefined rows have been removed, assuming they were real "NA" and not "0".
- Final dimensions are: 48 6 .
# A tibble: 48 x 6
   RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT  DEST 
          <dbl>       <dbl>    <dbl> <fct>    <fct> <fct>
 1        0.702    0.         1.06   TRUE     AW    WL   
 2        0.634    6.06e- 1   1.56   TRUE     BT    AW   
 3        0.713    7.58e- 2   1.50   TRUE     BT    GH   
 4        0.800    4.83e- 2   1.52   FALSE    BT    HT   
 5        0.782    8.92e- 4   2.14   TRUE     BT    LB   
 6        0.760    6.07e- 8   3.16   FALSE    BT    MI   
 7        0.801    1.83e- 1   1.56   FALSE    BT    NO   
 8        0.728    5.48e- 1   1.57   TRUE     BT    RT   
 9        0.728    9.60e- 1   0.921  FALSE    BT    WL   
10        0.764    5.75e- 2   2.25   TRUE     BT    ZB   
11        0.836    2.28e- 2   1.29   FALSE    CB    PL   
12        0.683    2.07e- 4   0.547  FALSE    CB    RC   
13        0.673    1.23e- 2   1.07   TRUE     GH    WL   
14        0.741    1.00e-11   2.79   TRUE     HN    CB   
15        0.840    0.         2.81   TRUE     HN    HT   
16        0.779    8.75e- 6   2.11   TRUE     HT    AW   
17        0.765    2.48e- 6   2.09   TRUE     HT    GH   
18        0.889    3.08e- 4   2.53   TRUE     HT    LB   
19        0.849    3.56e- 4   2.94   FALSE    HT    MI   
20        0.632    1.00e+ 0   0.0459 FALSE    HT    NO   
21        0.838    0.         2.74   TRUE     HT    PM   
22        0.701    3.59e- 6   2.23   TRUE     HT    RT   
23        0.653    7.49e- 4   1.55   FALSE    HT    WL   
24        0.874    2.57e-10   3.39   TRUE     HT    ZB   
25        0.735    8.05e- 6   1.94   FALSE    LB    CB   
26        0.721    3.73e- 4   1.50   TRUE     LB    MI   
27        0.745    0.         4.15   TRUE     MI    AW   
28        0.869    3.18e- 4   2.94   FALSE    MI    NO   
29        0.719    1.97e-11   3.38   TRUE     MI    OK   
30        0.785    0.         4.18   TRUE     MI    RT   
31        0.790    8.88e-16   3.49   TRUE     MI    ZB   
32        0.603    5.46e- 2   1.12   TRUE     RT    WL   
33        0.796    4.36e- 4   2.06   TRUE     SI    AD   
34        0.741    5.55e-16   4.01   TRUE     SI    AW   
35        0.743    4.13e- 9   3.18   TRUE     SI    BT   
36        0.737    9.94e-12   3.18   TRUE     SI    CB   
37        0.797    2.11e-15   3.93   TRUE     SI    GH   
38        0.761    7.13e- 1   0.576  TRUE     SI    HN   
39        0.842    6.99e- 4   2.81   TRUE     SI    HT   
40        0.727    6.40e- 3   1.55   TRUE     SI    LB   
41        0.858    4.30e- 3   2.80   TRUE     SI    NO   
42        0.706    1.91e-10   3.17   TRUE     SI    OK   
43        0.841    4.25e-14   3.86   TRUE     SI    PL   
44        0.781    3.82e- 9   2.54   TRUE     SI    PM   
45        0.699    1.30e- 8   2.94   TRUE     SI    RC   
46        0.777    2.22e-16   4.05   TRUE     SI    RT   
47        0.777    1.11e-15   3.51   TRUE     SI    ZB   
48        0.813    8.20e- 6   2.77   TRUE     WL    ZB   

Modelling function received variables: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Modelling function received variables: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .

Getting Model Summary: 
Linear mixed model fit by maximum likelihood  ['lmerMod']
Formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) +      (1 | DEST)
   Data: data_item

     AIC      BIC   logLik deviance df.resid 
  -132.2   -119.1     73.1   -146.2       41 

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-1.7977 -0.6370 -0.1495  0.6307  2.1004 

Random effects:
 Groups   Name        Variance  Std.Dev. 
 DEST     (Intercept) 1.370e-03 3.701e-02
 PORT     (Intercept) 1.251e-12 1.118e-06
 Residual             1.913e-03 4.374e-02
Number of obs: 48, groups:  DEST, 17; PORT, 11

Fixed effects:
              Estimate Std. Error t value
(Intercept)   0.718385   0.026708  26.898
B_FON_NOECO  -0.036111   0.034473  -1.048
PRED_ENV      0.028609   0.008647   3.308
ECO_DIFFTRUE -0.026066   0.018884  -1.380

Correlation of Fixed Effects:
            (Intr) B_FON_ PRED_E
B_FON_NOECO -0.549              
PRED_ENV    -0.705  0.492       
ECO_DIFFTRU -0.390  0.103 -0.224
convergence code: 0
boundary (singular) fit: see ?isSingular

Getting Model Coefficients from Summary: 
                Estimate  Std. Error   t value
(Intercept)   0.71838513 0.026708008 26.897743
B_FON_NOECO  -0.03611096 0.034473170 -1.047509
PRED_ENV      0.02860925 0.008647411  3.308418
ECO_DIFFTRUE -0.02606615 0.018883943 -1.380334

Getting Model ANOVA: 
Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + 
full_model:     (1 | DEST)
           Df     AIC     BIC logLik deviance  Chisq Chi Df Pr(>Chisq)
null_model  6 -133.12 -121.90 72.563  -145.12                         
full_model  7 -132.21 -119.11 73.103  -146.21 1.0802      1     0.2987

Plotting Model Coefficients: 
boundary (singular) fit: see ?isSingular

°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ 

Starting new analysis, with data index DIDX "11" and formula index FIDX "3" in Summary Tables.
Using formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 06_results_euk_otu99_shal_UNIF_model_data_2020-Jan-31-14-16-14_no_ph_with_hon_info.csv. 

Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.
- Setting types.
- Input dimensions are: 64 17 .
- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST VOY_FREQ B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .
- Kept variables are: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST .
- Intermediate dimensions are: 64 6 .
- Undefined rows have been removed, assuming they were real "NA" and not "0".
- Final dimensions are: 48 6 .
# A tibble: 48 x 6
   RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT  DEST 
          <dbl>       <dbl>    <dbl> <fct>    <fct> <fct>
 1        0.716    0.         1.06   TRUE     AW    WL   
 2        0.625    6.06e- 1   1.56   TRUE     BT    AW   
 3        0.705    7.58e- 2   1.50   TRUE     BT    GH   
 4        0.800    4.83e- 2   1.52   FALSE    BT    HT   
 5        0.777    8.92e- 4   2.14   TRUE     BT    LB   
 6        0.750    6.07e- 8   3.16   FALSE    BT    MI   
 7        0.795    1.83e- 1   1.56   FALSE    BT    NO   
 8        0.735    5.48e- 1   1.57   TRUE     BT    RT   
 9        0.733    9.60e- 1   0.921  FALSE    BT    WL   
10        0.776    5.75e- 2   2.25   TRUE     BT    ZB   
11        0.840    2.28e- 2   1.29   FALSE    CB    PL   
12        0.675    2.07e- 4   0.547  FALSE    CB    RC   
13        0.675    1.23e- 2   1.07   TRUE     GH    WL   
14        0.731    1.00e-11   2.79   TRUE     HN    CB   
15        0.836    0.         2.81   TRUE     HN    HT   
16        0.781    8.75e- 6   2.11   TRUE     HT    AW   
17        0.757    2.48e- 6   2.09   TRUE     HT    GH   
18        0.891    3.08e- 4   2.53   TRUE     HT    LB   
19        0.852    3.56e- 4   2.94   FALSE    HT    MI   
20        0.619    1.00e+ 0   0.0459 FALSE    HT    NO   
21        0.830    0.         2.74   TRUE     HT    PM   
22        0.694    3.59e- 6   2.23   TRUE     HT    RT   
23        0.642    7.49e- 4   1.55   FALSE    HT    WL   
24        0.878    2.57e-10   3.39   TRUE     HT    ZB   
25        0.724    8.05e- 6   1.94   FALSE    LB    CB   
26        0.710    3.73e- 4   1.50   TRUE     LB    MI   
27        0.745    0.         4.15   TRUE     MI    AW   
28        0.870    3.18e- 4   2.94   FALSE    MI    NO   
29        0.706    1.97e-11   3.38   TRUE     MI    OK   
30        0.793    0.         4.18   TRUE     MI    RT   
31        0.801    8.88e-16   3.49   TRUE     MI    ZB   
32        0.607    5.46e- 2   1.12   TRUE     RT    WL   
33        0.805    4.36e- 4   2.06   TRUE     SI    AD   
34        0.734    5.55e-16   4.01   TRUE     SI    AW   
35        0.741    4.13e- 9   3.18   TRUE     SI    BT   
36        0.736    9.94e-12   3.18   TRUE     SI    CB   
37        0.800    2.11e-15   3.93   TRUE     SI    GH   
38        0.765    7.13e- 1   0.576  TRUE     SI    HN   
39        0.843    6.99e- 4   2.81   TRUE     SI    HT   
40        0.721    6.40e- 3   1.55   TRUE     SI    LB   
41        0.857    4.30e- 3   2.80   TRUE     SI    NO   
42        0.706    1.91e-10   3.17   TRUE     SI    OK   
43        0.843    4.25e-14   3.86   TRUE     SI    PL   
44        0.784    3.82e- 9   2.54   TRUE     SI    PM   
45        0.692    1.30e- 8   2.94   TRUE     SI    RC   
46        0.777    2.22e-16   4.05   TRUE     SI    RT   
47        0.777    1.11e-15   3.51   TRUE     SI    ZB   
48        0.830    8.20e- 6   2.77   TRUE     WL    ZB   

Modelling function received variables: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Modelling function received variables: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Getting Model Summary: 
Linear mixed model fit by maximum likelihood  ['lmerMod']
Formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) +      (1 | DEST)
   Data: data_item

     AIC      BIC   logLik deviance df.resid 
  -125.8   -112.7     69.9   -139.8       41 

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-1.78515 -0.64950 -0.08552  0.71636  2.04577 

Random effects:
 Groups   Name        Variance Std.Dev.
 DEST     (Intercept) 0.001417 0.03764 
 PORT     (Intercept) 0.000000 0.00000 
 Residual             0.002249 0.04742 
Number of obs: 48, groups:  DEST, 17; PORT, 11

Fixed effects:
              Estimate Std. Error t value
(Intercept)   0.713165   0.028555  24.975
B_FON_NOECO  -0.035042   0.037181  -0.942
PRED_ENV      0.029323   0.009317   3.147
ECO_DIFFTRUE -0.023393   0.020320  -1.151

Correlation of Fixed Effects:
            (Intr) B_FON_ PRED_E
B_FON_NOECO -0.553              
PRED_ENV    -0.709  0.493       
ECO_DIFFTRU -0.389  0.100 -0.227
convergence code: 0
boundary (singular) fit: see ?isSingular

Getting Model Coefficients from Summary: 
                Estimate  Std. Error    t value
(Intercept)   0.71316479 0.028554834 24.9752733
B_FON_NOECO  -0.03504156 0.037180762 -0.9424647
PRED_ENV      0.02932307 0.009317481  3.1471033
ECO_DIFFTRUE -0.02339287 0.020320091 -1.1512186

Getting Model ANOVA: 
Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + 
full_model:     (1 | DEST)
           Df     AIC     BIC logLik deviance  Chisq Chi Df Pr(>Chisq)
null_model  6 -126.93 -115.70 69.464  -138.93                         
full_model  7 -125.80 -112.71 69.902  -139.80 0.8767      1     0.3491

Plotting Model Coefficients: 
boundary (singular) fit: see ?isSingular

°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ 

Starting new analysis, with data index DIDX "12" and formula index FIDX "3" in Summary Tables.
Using formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 06_results_euk_otu99_shal_UNIF_model_data_2020-Jan-31-14-16-14_with_hon_info.csv. 

Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.
- Setting types.
- Input dimensions are: 69 17 .
- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST VOY_FREQ B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .
- Kept variables are: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST .
- Intermediate dimensions are: 69 6 .
- Undefined rows have been removed, assuming they were real "NA" and not "0".
- Final dimensions are: 48 6 .
# A tibble: 48 x 6
   RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT  DEST 
          <dbl>       <dbl>    <dbl> <fct>    <fct> <fct>
 1        0.716    0.         1.06   TRUE     AW    WL   
 2        0.625    6.06e- 1   1.56   TRUE     BT    AW   
 3        0.705    7.58e- 2   1.50   TRUE     BT    GH   
 4        0.800    4.83e- 2   1.52   FALSE    BT    HT   
 5        0.777    8.92e- 4   2.14   TRUE     BT    LB   
 6        0.750    6.07e- 8   3.16   FALSE    BT    MI   
 7        0.795    1.83e- 1   1.56   FALSE    BT    NO   
 8        0.735    5.48e- 1   1.57   TRUE     BT    RT   
 9        0.733    9.60e- 1   0.921  FALSE    BT    WL   
10        0.776    5.75e- 2   2.25   TRUE     BT    ZB   
11        0.840    2.28e- 2   1.29   FALSE    CB    PL   
12        0.675    2.07e- 4   0.547  FALSE    CB    RC   
13        0.675    1.23e- 2   1.07   TRUE     GH    WL   
14        0.731    1.00e-11   2.79   TRUE     HN    CB   
15        0.836    0.         2.81   TRUE     HN    HT   
16        0.781    8.75e- 6   2.11   TRUE     HT    AW   
17        0.757    2.48e- 6   2.09   TRUE     HT    GH   
18        0.891    3.08e- 4   2.53   TRUE     HT    LB   
19        0.852    3.56e- 4   2.94   FALSE    HT    MI   
20        0.619    1.00e+ 0   0.0459 FALSE    HT    NO   
21        0.830    0.         2.74   TRUE     HT    PM   
22        0.694    3.59e- 6   2.23   TRUE     HT    RT   
23        0.642    7.49e- 4   1.55   FALSE    HT    WL   
24        0.878    2.57e-10   3.39   TRUE     HT    ZB   
25        0.724    8.05e- 6   1.94   FALSE    LB    CB   
26        0.710    3.73e- 4   1.50   TRUE     LB    MI   
27        0.745    0.         4.15   TRUE     MI    AW   
28        0.870    3.18e- 4   2.94   FALSE    MI    NO   
29        0.706    1.97e-11   3.38   TRUE     MI    OK   
30        0.793    0.         4.18   TRUE     MI    RT   
31        0.801    8.88e-16   3.49   TRUE     MI    ZB   
32        0.607    5.46e- 2   1.12   TRUE     RT    WL   
33        0.805    4.36e- 4   2.06   TRUE     SI    AD   
34        0.734    5.55e-16   4.01   TRUE     SI    AW   
35        0.741    4.13e- 9   3.18   TRUE     SI    BT   
36        0.736    9.94e-12   3.18   TRUE     SI    CB   
37        0.800    2.11e-15   3.93   TRUE     SI    GH   
38        0.765    7.13e- 1   0.576  TRUE     SI    HN   
39        0.843    6.99e- 4   2.81   TRUE     SI    HT   
40        0.721    6.40e- 3   1.55   TRUE     SI    LB   
41        0.857    4.30e- 3   2.80   TRUE     SI    NO   
42        0.706    1.91e-10   3.17   TRUE     SI    OK   
43        0.843    4.25e-14   3.86   TRUE     SI    PL   
44        0.784    3.82e- 9   2.54   TRUE     SI    PM   
45        0.692    1.30e- 8   2.94   TRUE     SI    RC   
46        0.777    2.22e-16   4.05   TRUE     SI    RT   
47        0.777    1.11e-15   3.51   TRUE     SI    ZB   
48        0.830    8.20e- 6   2.77   TRUE     WL    ZB   

Modelling function received variables: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Modelling function received variables: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Getting Model Summary: 
Linear mixed model fit by maximum likelihood  ['lmerMod']
Formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) +      (1 | DEST)
   Data: data_item

     AIC      BIC   logLik deviance df.resid 
  -125.8   -112.7     69.9   -139.8       41 

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-1.78515 -0.64950 -0.08552  0.71636  2.04577 

Random effects:
 Groups   Name        Variance Std.Dev.
 DEST     (Intercept) 0.001417 0.03764 
 PORT     (Intercept) 0.000000 0.00000 
 Residual             0.002249 0.04742 
Number of obs: 48, groups:  DEST, 17; PORT, 11

Fixed effects:
              Estimate Std. Error t value
(Intercept)   0.713165   0.028555  24.975
B_FON_NOECO  -0.035042   0.037181  -0.942
PRED_ENV      0.029323   0.009317   3.147
ECO_DIFFTRUE -0.023393   0.020320  -1.151

Correlation of Fixed Effects:
            (Intr) B_FON_ PRED_E
B_FON_NOECO -0.553              
PRED_ENV    -0.709  0.493       
ECO_DIFFTRU -0.389  0.100 -0.227
convergence code: 0
boundary (singular) fit: see ?isSingular

Getting Model Coefficients from Summary: 
                Estimate  Std. Error    t value
(Intercept)   0.71316479 0.028554834 24.9752733
B_FON_NOECO  -0.03504156 0.037180762 -0.9424647
PRED_ENV      0.02932307 0.009317481  3.1471033
ECO_DIFFTRUE -0.02339287 0.020320091 -1.1512186

Getting Model ANOVA: 
Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + 
full_model:     (1 | DEST)
           Df     AIC     BIC logLik deviance  Chisq Chi Df Pr(>Chisq)
null_model  6 -126.93 -115.70 69.464  -138.93                         
full_model  7 -125.80 -112.71 69.902  -139.80 0.8767      1     0.3491

Plotting Model Coefficients: 
boundary (singular) fit: see ?isSingular

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Starting new analysis, with data index DIDX "13" and formula index FIDX "3" in Summary Tables.
Using formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 07_results_euk_asv00_shal_JAQU_model_data_2020-Jan-31-14-16-25_no_ph_with_hon_info.csv. 

Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.
- Setting types.
- Input dimensions are: 64 17 .
- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST VOY_FREQ B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .
- Kept variables are: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST .
- Intermediate dimensions are: 64 6 .
- Undefined rows have been removed, assuming they were real "NA" and not "0".
- Final dimensions are: 48 6 .
# A tibble: 48 x 6
   RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT  DEST 
          <dbl>       <dbl>    <dbl> <fct>    <fct> <fct>
 1        0.951    0.         1.06   TRUE     AW    WL   
 2        0.902    6.06e- 1   1.56   TRUE     BT    AW   
 3        0.943    7.58e- 2   1.50   TRUE     BT    GH   
 4        0.989    4.83e- 2   1.52   FALSE    BT    HT   
 5        0.964    8.92e- 4   2.14   TRUE     BT    LB   
 6        0.974    6.07e- 8   3.16   FALSE    BT    MI   
 7        0.985    1.83e- 1   1.56   FALSE    BT    NO   
 8        0.955    5.48e- 1   1.57   TRUE     BT    RT   
 9        0.957    9.60e- 1   0.921  FALSE    BT    WL   
10        0.956    5.75e- 2   2.25   TRUE     BT    ZB   
11        1        2.28e- 2   1.29   FALSE    CB    PL   
12        0.907    2.07e- 4   0.547  FALSE    CB    RC   
13        0.913    1.23e- 2   1.07   TRUE     GH    WL   
14        0.980    1.00e-11   2.79   TRUE     HN    CB   
15        0.993    0.         2.81   TRUE     HN    HT   
16        0.988    8.75e- 6   2.11   TRUE     HT    AW   
17        0.969    2.48e- 6   2.09   TRUE     HT    GH   
18        1.00     3.08e- 4   2.53   TRUE     HT    LB   
19        0.994    3.56e- 4   2.94   FALSE    HT    MI   
20        0.891    1.00e+ 0   0.0459 FALSE    HT    NO   
21        0.997    0.         2.74   TRUE     HT    PM   
22        0.948    3.59e- 6   2.23   TRUE     HT    RT   
23        0.905    7.49e- 4   1.55   FALSE    HT    WL   
24        0.998    2.57e-10   3.39   TRUE     HT    ZB   
25        0.939    8.05e- 6   1.94   FALSE    LB    CB   
26        0.942    3.73e- 4   1.50   TRUE     LB    MI   
27        0.988    0.         4.15   TRUE     MI    AW   
28        0.998    3.18e- 4   2.94   FALSE    MI    NO   
29        0.962    1.97e-11   3.38   TRUE     MI    OK   
30        0.991    0.         4.18   TRUE     MI    RT   
31        0.988    8.88e-16   3.49   TRUE     MI    ZB   
32        0.894    5.46e- 2   1.12   TRUE     RT    WL   
33        0.971    4.36e- 4   2.06   TRUE     SI    AD   
34        0.986    5.55e-16   4.01   TRUE     SI    AW   
35        0.973    4.13e- 9   3.18   TRUE     SI    BT   
36        0.982    9.94e-12   3.18   TRUE     SI    CB   
37        0.996    2.11e-15   3.93   TRUE     SI    GH   
38        0.958    7.13e- 1   0.576  TRUE     SI    HN   
39        0.997    6.99e- 4   2.81   TRUE     SI    HT   
40        0.966    6.40e- 3   1.55   TRUE     SI    LB   
41        0.998    4.30e- 3   2.80   TRUE     SI    NO   
42        0.959    1.91e-10   3.17   TRUE     SI    OK   
43        0.998    4.25e-14   3.86   TRUE     SI    PL   
44        0.996    3.82e- 9   2.54   TRUE     SI    PM   
45        0.967    1.30e- 8   2.94   TRUE     SI    RC   
46        0.993    2.22e-16   4.05   TRUE     SI    RT   
47        0.986    1.11e-15   3.51   TRUE     SI    ZB   
48        0.996    8.20e- 6   2.77   TRUE     WL    ZB   

Modelling function received variables: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Modelling function received variables: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Getting Model Summary: 
Linear mixed model fit by maximum likelihood  ['lmerMod']
Formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) +      (1 | DEST)
   Data: data_item

     AIC      BIC   logLik deviance df.resid 
  -215.9   -202.8    115.0   -229.9       41 

Scaled residuals: 
   Min     1Q Median     3Q    Max 
-2.069 -0.475 -0.122  0.617  1.951 

Random effects:
 Groups   Name        Variance  Std.Dev.
 DEST     (Intercept) 8.983e-05 0.009478
 PORT     (Intercept) 0.000e+00 0.000000
 Residual             4.127e-04 0.020315
Number of obs: 48, groups:  DEST, 17; PORT, 11

Fixed effects:
              Estimate Std. Error t value
(Intercept)   0.928304   0.011078  83.796
B_FON_NOECO  -0.009205   0.015287  -0.602
PRED_ENV      0.019296   0.003783   5.100
ECO_DIFFTRUE -0.005945   0.008185  -0.726

Correlation of Fixed Effects:
            (Intr) B_FON_ PRED_E
B_FON_NOECO -0.570              
PRED_ENV    -0.732  0.495       
ECO_DIFFTRU -0.374  0.080 -0.254
convergence code: 0
boundary (singular) fit: see ?isSingular

Getting Model Coefficients from Summary: 
                 Estimate  Std. Error    t value
(Intercept)   0.928303817 0.011078149 83.7959281
B_FON_NOECO  -0.009204673 0.015286957 -0.6021259
PRED_ENV      0.019295847 0.003783291  5.1002812
ECO_DIFFTRUE -0.005944778 0.008185241 -0.7262801

Getting Model ANOVA: 
Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + 
full_model:     (1 | DEST)
           Df     AIC     BIC logLik deviance  Chisq Chi Df Pr(>Chisq)
null_model  6 -217.57 -206.34 114.79  -229.57                         
full_model  7 -215.93 -202.84 114.97  -229.93 0.3612      1     0.5478

Plotting Model Coefficients: 
boundary (singular) fit: see ?isSingular

°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ 

Starting new analysis, with data index DIDX "14" and formula index FIDX "3" in Summary Tables.
Using formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 07_results_euk_asv00_shal_JAQU_model_data_2020-Jan-31-14-16-25_with_hon_info.csv. 

Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.
- Setting types.
- Input dimensions are: 69 17 .
- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST VOY_FREQ B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .
- Kept variables are: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST .
- Intermediate dimensions are: 69 6 .
- Undefined rows have been removed, assuming they were real "NA" and not "0".
- Final dimensions are: 48 6 .
# A tibble: 48 x 6
   RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT  DEST 
          <dbl>       <dbl>    <dbl> <fct>    <fct> <fct>
 1        0.951    0.         1.06   TRUE     AW    WL   
 2        0.902    6.06e- 1   1.56   TRUE     BT    AW   
 3        0.943    7.58e- 2   1.50   TRUE     BT    GH   
 4        0.989    4.83e- 2   1.52   FALSE    BT    HT   
 5        0.964    8.92e- 4   2.14   TRUE     BT    LB   
 6        0.974    6.07e- 8   3.16   FALSE    BT    MI   
 7        0.985    1.83e- 1   1.56   FALSE    BT    NO   
 8        0.955    5.48e- 1   1.57   TRUE     BT    RT   
 9        0.957    9.60e- 1   0.921  FALSE    BT    WL   
10        0.956    5.75e- 2   2.25   TRUE     BT    ZB   
11        1        2.28e- 2   1.29   FALSE    CB    PL   
12        0.907    2.07e- 4   0.547  FALSE    CB    RC   
13        0.913    1.23e- 2   1.07   TRUE     GH    WL   
14        0.980    1.00e-11   2.79   TRUE     HN    CB   
15        0.993    0.         2.81   TRUE     HN    HT   
16        0.988    8.75e- 6   2.11   TRUE     HT    AW   
17        0.969    2.48e- 6   2.09   TRUE     HT    GH   
18        1.00     3.08e- 4   2.53   TRUE     HT    LB   
19        0.994    3.56e- 4   2.94   FALSE    HT    MI   
20        0.891    1.00e+ 0   0.0459 FALSE    HT    NO   
21        0.997    0.         2.74   TRUE     HT    PM   
22        0.948    3.59e- 6   2.23   TRUE     HT    RT   
23        0.905    7.49e- 4   1.55   FALSE    HT    WL   
24        0.998    2.57e-10   3.39   TRUE     HT    ZB   
25        0.939    8.05e- 6   1.94   FALSE    LB    CB   
26        0.942    3.73e- 4   1.50   TRUE     LB    MI   
27        0.988    0.         4.15   TRUE     MI    AW   
28        0.998    3.18e- 4   2.94   FALSE    MI    NO   
29        0.962    1.97e-11   3.38   TRUE     MI    OK   
30        0.991    0.         4.18   TRUE     MI    RT   
31        0.988    8.88e-16   3.49   TRUE     MI    ZB   
32        0.894    5.46e- 2   1.12   TRUE     RT    WL   
33        0.971    4.36e- 4   2.06   TRUE     SI    AD   
34        0.986    5.55e-16   4.01   TRUE     SI    AW   
35        0.973    4.13e- 9   3.18   TRUE     SI    BT   
36        0.982    9.94e-12   3.18   TRUE     SI    CB   
37        0.996    2.11e-15   3.93   TRUE     SI    GH   
38        0.958    7.13e- 1   0.576  TRUE     SI    HN   
39        0.997    6.99e- 4   2.81   TRUE     SI    HT   
40        0.966    6.40e- 3   1.55   TRUE     SI    LB   
41        0.998    4.30e- 3   2.80   TRUE     SI    NO   
42        0.959    1.91e-10   3.17   TRUE     SI    OK   
43        0.998    4.25e-14   3.86   TRUE     SI    PL   
44        0.996    3.82e- 9   2.54   TRUE     SI    PM   
45        0.967    1.30e- 8   2.94   TRUE     SI    RC   
46        0.993    2.22e-16   4.05   TRUE     SI    RT   
47        0.986    1.11e-15   3.51   TRUE     SI    ZB   
48        0.996    8.20e- 6   2.77   TRUE     WL    ZB   

Modelling function received variables: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Modelling function received variables: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Getting Model Summary: 
Linear mixed model fit by maximum likelihood  ['lmerMod']
Formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) +      (1 | DEST)
   Data: data_item

     AIC      BIC   logLik deviance df.resid 
  -215.9   -202.8    115.0   -229.9       41 

Scaled residuals: 
   Min     1Q Median     3Q    Max 
-2.069 -0.475 -0.122  0.617  1.951 

Random effects:
 Groups   Name        Variance  Std.Dev.
 DEST     (Intercept) 8.983e-05 0.009478
 PORT     (Intercept) 0.000e+00 0.000000
 Residual             4.127e-04 0.020315
Number of obs: 48, groups:  DEST, 17; PORT, 11

Fixed effects:
              Estimate Std. Error t value
(Intercept)   0.928304   0.011078  83.796
B_FON_NOECO  -0.009205   0.015287  -0.602
PRED_ENV      0.019296   0.003783   5.100
ECO_DIFFTRUE -0.005945   0.008185  -0.726

Correlation of Fixed Effects:
            (Intr) B_FON_ PRED_E
B_FON_NOECO -0.570              
PRED_ENV    -0.732  0.495       
ECO_DIFFTRU -0.374  0.080 -0.254
convergence code: 0
boundary (singular) fit: see ?isSingular

Getting Model Coefficients from Summary: 
                 Estimate  Std. Error    t value
(Intercept)   0.928303817 0.011078149 83.7959281
B_FON_NOECO  -0.009204673 0.015286957 -0.6021259
PRED_ENV      0.019295847 0.003783291  5.1002812
ECO_DIFFTRUE -0.005944778 0.008185241 -0.7262801

Getting Model ANOVA: 
Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + 
full_model:     (1 | DEST)
           Df     AIC     BIC logLik deviance  Chisq Chi Df Pr(>Chisq)
null_model  6 -217.57 -206.34 114.79  -229.57                         
full_model  7 -215.93 -202.84 114.97  -229.93 0.3612      1     0.5478

Plotting Model Coefficients: 
boundary (singular) fit: see ?isSingular

°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ 

Starting new analysis, with data index DIDX "15" and formula index FIDX "3" in Summary Tables.
Using formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 08_results_euk_otu99_shal_JAQU_model_data_2020-Jan-31-14-16-36_no_ph_with_hon_info.csv. 

Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.
- Setting types.
- Input dimensions are: 64 17 .
- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST VOY_FREQ B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .
- Kept variables are: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST .
- Intermediate dimensions are: 64 6 .
- Undefined rows have been removed, assuming they were real "NA" and not "0".
- Final dimensions are: 48 6 .
# A tibble: 48 x 6
   RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT  DEST 
          <dbl>       <dbl>    <dbl> <fct>    <fct> <fct>
 1        0.919    0.         1.06   TRUE     AW    WL   
 2        0.847    6.06e- 1   1.56   TRUE     BT    AW   
 3        0.906    7.58e- 2   1.50   TRUE     BT    GH   
 4        0.980    4.83e- 2   1.52   FALSE    BT    HT   
 5        0.936    8.92e- 4   2.14   TRUE     BT    LB   
 6        0.933    6.07e- 8   3.16   FALSE    BT    MI   
 7        0.971    1.83e- 1   1.56   FALSE    BT    NO   
 8        0.927    5.48e- 1   1.57   TRUE     BT    RT   
 9        0.934    9.60e- 1   0.921  FALSE    BT    WL   
10        0.931    5.75e- 2   2.25   TRUE     BT    ZB   
11        0.998    2.28e- 2   1.29   FALSE    CB    PL   
12        0.863    2.07e- 4   0.547  FALSE    CB    RC   
13        0.879    1.23e- 2   1.07   TRUE     GH    WL   
14        0.947    1.00e-11   2.79   TRUE     HN    CB   
15        0.988    0.         2.81   TRUE     HN    HT   
16        0.981    8.75e- 6   2.11   TRUE     HT    AW   
17        0.951    2.48e- 6   2.09   TRUE     HT    GH   
18        0.998    3.08e- 4   2.53   TRUE     HT    LB   
19        0.987    3.56e- 4   2.94   FALSE    HT    MI   
20        0.848    1.00e+ 0   0.0459 FALSE    HT    NO   
21        0.995    0.         2.74   TRUE     HT    PM   
22        0.920    3.59e- 6   2.23   TRUE     HT    RT   
23        0.865    7.49e- 4   1.55   FALSE    HT    WL   
24        0.997    2.57e-10   3.39   TRUE     HT    ZB   
25        0.901    8.05e- 6   1.94   FALSE    LB    CB   
26        0.907    3.73e- 4   1.50   TRUE     LB    MI   
27        0.966    0.         4.15   TRUE     MI    AW   
28        0.990    3.18e- 4   2.94   FALSE    MI    NO   
29        0.927    1.97e-11   3.38   TRUE     MI    OK   
30        0.976    0.         4.18   TRUE     MI    RT   
31        0.965    8.88e-16   3.49   TRUE     MI    ZB   
32        0.839    5.46e- 2   1.12   TRUE     RT    WL   
33        0.945    4.36e- 4   2.06   TRUE     SI    AD   
34        0.960    5.55e-16   4.01   TRUE     SI    AW   
35        0.947    4.13e- 9   3.18   TRUE     SI    BT   
36        0.950    9.94e-12   3.18   TRUE     SI    CB   
37        0.986    2.11e-15   3.93   TRUE     SI    GH   
38        0.935    7.13e- 1   0.576  TRUE     SI    HN   
39        0.994    6.99e- 4   2.81   TRUE     SI    HT   
40        0.931    6.40e- 3   1.55   TRUE     SI    LB   
41        0.995    4.30e- 3   2.80   TRUE     SI    NO   
42        0.919    1.91e-10   3.17   TRUE     SI    OK   
43        0.997    4.25e-14   3.86   TRUE     SI    PL   
44        0.983    3.82e- 9   2.54   TRUE     SI    PM   
45        0.928    1.30e- 8   2.94   TRUE     SI    RC   
46        0.976    2.22e-16   4.05   TRUE     SI    RT   
47        0.952    1.11e-15   3.51   TRUE     SI    ZB   
48        0.987    8.20e- 6   2.77   TRUE     WL    ZB   

Modelling function received variables: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Modelling function received variables: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Getting Model Summary: 
Linear mixed model fit by maximum likelihood  ['lmerMod']
Formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) +      (1 | DEST)
   Data: data_item

     AIC      BIC   logLik deviance df.resid 
  -174.8   -161.7     94.4   -188.8       41 

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-1.93141 -0.51558 -0.09184  0.81272  1.77162 

Random effects:
 Groups   Name        Variance  Std.Dev.
 DEST     (Intercept) 0.0003072 0.01753 
 PORT     (Intercept) 0.0000000 0.00000 
 Residual             0.0009117 0.03019 
Number of obs: 48, groups:  DEST, 17; PORT, 11

Fixed effects:
              Estimate Std. Error t value
(Intercept)   0.896467   0.017088  52.463
B_FON_NOECO  -0.014710   0.023088  -0.637
PRED_ENV      0.024481   0.005748   4.259
ECO_DIFFTRUE -0.010036   0.012466  -0.805

Correlation of Fixed Effects:
            (Intr) B_FON_ PRED_E
B_FON_NOECO -0.565              
PRED_ENV    -0.725  0.495       
ECO_DIFFTRU -0.381  0.087 -0.243
convergence code: 0
boundary (singular) fit: see ?isSingular

Getting Model Coefficients from Summary: 
                Estimate  Std. Error    t value
(Intercept)   0.89646675 0.017087632 52.4628999
B_FON_NOECO  -0.01471004 0.023088437 -0.6371172
PRED_ENV      0.02448060 0.005747735  4.2591730
ECO_DIFFTRUE -0.01003636 0.012465984 -0.8051000

Getting Model ANOVA: 
Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + 
full_model:     (1 | DEST)
           Df     AIC     BIC logLik deviance  Chisq Chi Df Pr(>Chisq)
null_model  6 -176.43 -165.20 94.215  -188.43                         
full_model  7 -174.83 -161.74 94.417  -188.83 0.4026      1     0.5258

Plotting Model Coefficients: 
boundary (singular) fit: see ?isSingular

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Starting new analysis, with data index DIDX "16" and formula index FIDX "3" in Summary Tables.
Using formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 08_results_euk_otu99_shal_JAQU_model_data_2020-Jan-31-14-16-36_with_hon_info.csv. 

Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.
- Setting types.
- Input dimensions are: 69 17 .
- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST VOY_FREQ B_HON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .
- Kept variables are: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST .
- Intermediate dimensions are: 69 6 .
- Undefined rows have been removed, assuming they were real "NA" and not "0".
- Final dimensions are: 48 6 .
# A tibble: 48 x 6
   RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT  DEST 
          <dbl>       <dbl>    <dbl> <fct>    <fct> <fct>
 1        0.919    0.         1.06   TRUE     AW    WL   
 2        0.847    6.06e- 1   1.56   TRUE     BT    AW   
 3        0.906    7.58e- 2   1.50   TRUE     BT    GH   
 4        0.980    4.83e- 2   1.52   FALSE    BT    HT   
 5        0.936    8.92e- 4   2.14   TRUE     BT    LB   
 6        0.933    6.07e- 8   3.16   FALSE    BT    MI   
 7        0.971    1.83e- 1   1.56   FALSE    BT    NO   
 8        0.927    5.48e- 1   1.57   TRUE     BT    RT   
 9        0.934    9.60e- 1   0.921  FALSE    BT    WL   
10        0.931    5.75e- 2   2.25   TRUE     BT    ZB   
11        0.998    2.28e- 2   1.29   FALSE    CB    PL   
12        0.863    2.07e- 4   0.547  FALSE    CB    RC   
13        0.879    1.23e- 2   1.07   TRUE     GH    WL   
14        0.947    1.00e-11   2.79   TRUE     HN    CB   
15        0.988    0.         2.81   TRUE     HN    HT   
16        0.981    8.75e- 6   2.11   TRUE     HT    AW   
17        0.951    2.48e- 6   2.09   TRUE     HT    GH   
18        0.998    3.08e- 4   2.53   TRUE     HT    LB   
19        0.987    3.56e- 4   2.94   FALSE    HT    MI   
20        0.848    1.00e+ 0   0.0459 FALSE    HT    NO   
21        0.995    0.         2.74   TRUE     HT    PM   
22        0.920    3.59e- 6   2.23   TRUE     HT    RT   
23        0.865    7.49e- 4   1.55   FALSE    HT    WL   
24        0.997    2.57e-10   3.39   TRUE     HT    ZB   
25        0.901    8.05e- 6   1.94   FALSE    LB    CB   
26        0.907    3.73e- 4   1.50   TRUE     LB    MI   
27        0.966    0.         4.15   TRUE     MI    AW   
28        0.990    3.18e- 4   2.94   FALSE    MI    NO   
29        0.927    1.97e-11   3.38   TRUE     MI    OK   
30        0.976    0.         4.18   TRUE     MI    RT   
31        0.965    8.88e-16   3.49   TRUE     MI    ZB   
32        0.839    5.46e- 2   1.12   TRUE     RT    WL   
33        0.945    4.36e- 4   2.06   TRUE     SI    AD   
34        0.960    5.55e-16   4.01   TRUE     SI    AW   
35        0.947    4.13e- 9   3.18   TRUE     SI    BT   
36        0.950    9.94e-12   3.18   TRUE     SI    CB   
37        0.986    2.11e-15   3.93   TRUE     SI    GH   
38        0.935    7.13e- 1   0.576  TRUE     SI    HN   
39        0.994    6.99e- 4   2.81   TRUE     SI    HT   
40        0.931    6.40e- 3   1.55   TRUE     SI    LB   
41        0.995    4.30e- 3   2.80   TRUE     SI    NO   
42        0.919    1.91e-10   3.17   TRUE     SI    OK   
43        0.997    4.25e-14   3.86   TRUE     SI    PL   
44        0.983    3.82e- 9   2.54   TRUE     SI    PM   
45        0.928    1.30e- 8   2.94   TRUE     SI    RC   
46        0.976    2.22e-16   4.05   TRUE     SI    RT   
47        0.952    1.11e-15   3.51   TRUE     SI    ZB   
48        0.987    8.20e- 6   2.77   TRUE     WL    ZB   

Modelling function received variables: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Modelling function received variables: RESP_UNIFRAC B_FON_NOECO PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Getting Model Summary: 
Linear mixed model fit by maximum likelihood  ['lmerMod']
Formula: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) +      (1 | DEST)
   Data: data_item

     AIC      BIC   logLik deviance df.resid 
  -174.8   -161.7     94.4   -188.8       41 

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-1.93141 -0.51558 -0.09184  0.81272  1.77162 

Random effects:
 Groups   Name        Variance  Std.Dev.
 DEST     (Intercept) 0.0003072 0.01753 
 PORT     (Intercept) 0.0000000 0.00000 
 Residual             0.0009117 0.03019 
Number of obs: 48, groups:  DEST, 17; PORT, 11

Fixed effects:
              Estimate Std. Error t value
(Intercept)   0.896467   0.017088  52.463
B_FON_NOECO  -0.014710   0.023088  -0.637
PRED_ENV      0.024481   0.005748   4.259
ECO_DIFFTRUE -0.010036   0.012466  -0.805

Correlation of Fixed Effects:
            (Intr) B_FON_ PRED_E
B_FON_NOECO -0.565              
PRED_ENV    -0.725  0.495       
ECO_DIFFTRU -0.381  0.087 -0.243
convergence code: 0
boundary (singular) fit: see ?isSingular

Getting Model Coefficients from Summary: 
                Estimate  Std. Error    t value
(Intercept)   0.89646675 0.017087632 52.4628999
B_FON_NOECO  -0.01471004 0.023088437 -0.6371172
PRED_ENV      0.02448060 0.005747735  4.2591730
ECO_DIFFTRUE -0.01003636 0.012465984 -0.8051000

Getting Model ANOVA: 
Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + 
full_model:     (1 | DEST)
           Df     AIC     BIC logLik deviance  Chisq Chi Df Pr(>Chisq)
null_model  6 -176.43 -165.20 94.215  -188.43                         
full_model  7 -174.83 -161.74 94.417  -188.83 0.4026      1     0.5258

Plotting Model Coefficients: 
boundary (singular) fit: see ?isSingular

°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ 

Starting new analysis, with data index DIDX "1" and formula index FIDX "4" in Summary Tables.
Using formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 01_results_euk_asv00_deep_UNIF_model_data_2020-Jan-31-14-15-13_no_ph_with_hon_info.csv. 

Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.
- Setting types.
- Input dimensions are: 64 17 .
- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST VOY_FREQ B_FON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .
- Kept variables are: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST .
- Intermediate dimensions are: 64 6 .
- Undefined rows have been removed, assuming they were real "NA" and not "0".
- Final dimensions are: 48 6 .
# A tibble: 48 x 6
   RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT  DEST 
          <dbl>       <dbl>    <dbl> <fct>    <fct> <fct>
 1        0.700    1.14e- 4   1.06   TRUE     AW    WL   
 2        0.636    5.55e- 3   1.56   TRUE     BT    AW   
 3        0.695    6.70e- 4   1.50   TRUE     BT    GH   
 4        0.794    1.01e- 4   1.52   FALSE    BT    HT   
 5        0.785    1.02e- 5   2.14   TRUE     BT    LB   
 6        0.756    9.45e-11   3.16   FALSE    BT    MI   
 7        0.794    1.77e- 4   1.56   FALSE    BT    NO   
 8        0.732    2.80e- 3   1.57   TRUE     BT    RT   
 9        0.723    8.76e- 3   0.921  FALSE    BT    WL   
10        0.776    1.49e- 4   2.25   TRUE     BT    ZB   
11        0.832    1.48e- 5   1.29   FALSE    CB    PL   
12        0.683    4.06e- 5   0.547  FALSE    CB    RC   
13        0.654    2.96e- 4   1.07   TRUE     GH    WL   
14        0.739    3.92e-12   2.79   TRUE     HN    CB   
15        0.829    0.         2.81   TRUE     HN    HT   
16        0.774    2.51e- 8   2.11   TRUE     HT    AW   
17        0.747    8.88e- 8   2.09   TRUE     HT    GH   
18        0.885    1.47e- 6   2.53   TRUE     HT    LB   
19        0.845    8.29e- 7   2.94   FALSE    HT    MI   
20        0.628    1.00e+ 0   0.0459 FALSE    HT    NO   
21        0.828    0.         2.74   TRUE     HT    PM   
22        0.695    1.60e- 8   2.23   TRUE     HT    RT   
23        0.639    3.73e- 6   1.55   FALSE    HT    WL   
24        0.869    0.         3.39   TRUE     HT    ZB   
25        0.738    1.26e- 7   1.94   FALSE    LB    CB   
26        0.726    8.69e- 6   1.50   TRUE     LB    MI   
27        0.748    0.         4.15   TRUE     MI    AW   
28        0.864    1.06e- 6   2.94   FALSE    MI    NO   
29        0.712    0.         3.38   TRUE     MI    OK   
30        0.786    0.         4.18   TRUE     MI    RT   
31        0.799    0.         3.49   TRUE     MI    ZB   
32        0.603    1.44e- 3   1.12   TRUE     RT    WL   
33        0.815    4.13e- 6   2.06   TRUE     SI    AD   
34        0.740    0.         4.01   TRUE     SI    AW   
35        0.748    6.55e-11   3.18   TRUE     SI    BT   
36        0.724    0.         3.18   TRUE     SI    CB   
37        0.789    0.         3.93   TRUE     SI    GH   
38        0.762    8.94e- 3   0.576  TRUE     SI    HN   
39        0.836    1.00e- 5   2.81   TRUE     SI    HT   
40        0.730    9.72e- 5   1.55   TRUE     SI    LB   
41        0.853    3.21e- 5   2.80   TRUE     SI    NO   
42        0.692    1.75e-12   3.17   TRUE     SI    OK   
43        0.835    0.         3.86   TRUE     SI    PL   
44        0.780    1.05e-10   2.54   TRUE     SI    PM   
45        0.691    1.44e-10   2.94   TRUE     SI    RC   
46        0.774    0.         4.05   TRUE     SI    RT   
47        0.789    0.         3.51   TRUE     SI    ZB   
48        0.817    4.24e- 7   2.77   TRUE     WL    ZB   

Modelling function received variables: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .

Modelling function received variables: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Getting Model Summary: 
Linear mixed model fit by maximum likelihood  ['lmerMod']
Formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) +      (1 | DEST)
   Data: data_item

     AIC      BIC   logLik deviance df.resid 
  -140.1   -127.0     77.0   -154.1       41 

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-1.90179 -0.46056 -0.08439  0.59392  2.02672 

Random effects:
 Groups   Name        Variance  Std.Dev.
 DEST     (Intercept) 2.077e-03 0.045572
 PORT     (Intercept) 9.894e-05 0.009947
 Residual             1.252e-03 0.035384
Number of obs: 48, groups:  DEST, 17; PORT, 11

Fixed effects:
              Estimate Std. Error t value
(Intercept)   0.722667   0.023240  31.096
B_HON_NOECO  -0.158283   0.044268  -3.576
PRED_ENV      0.025646   0.007243   3.541
ECO_DIFFTRUE -0.023867   0.016533  -1.444

Correlation of Fixed Effects:
            (Intr) B_HON_ PRED_E
B_HON_NOECO -0.321              
PRED_ENV    -0.621  0.352       
ECO_DIFFTRU -0.415  0.066 -0.189

Getting Model Coefficients from Summary: 
                Estimate  Std. Error   t value
(Intercept)   0.72266677 0.023239743 31.096160
B_HON_NOECO  -0.15828288 0.044268219 -3.575542
PRED_ENV      0.02564636 0.007242979  3.540858
ECO_DIFFTRUE -0.02386716 0.016533016 -1.443606

Getting Model ANOVA: 
Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + 
full_model:     (1 | DEST)
           Df     AIC     BIC logLik deviance  Chisq Chi Df Pr(>Chisq)   
null_model  6 -133.05 -121.82 72.525  -145.05                            
full_model  7 -140.07 -126.98 77.037  -154.07 9.0252      1   0.002663 **
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Plotting Model Coefficients: 

°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ 

Starting new analysis, with data index DIDX "2" and formula index FIDX "4" in Summary Tables.
Using formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 01_results_euk_asv00_deep_UNIF_model_data_2020-Jan-31-14-15-13_with_hon_info.csv. 

Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.
- Setting types.
- Input dimensions are: 69 17 .
- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST VOY_FREQ B_FON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .
- Kept variables are: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST .
- Intermediate dimensions are: 69 6 .
- Undefined rows have been removed, assuming they were real "NA" and not "0".
- Final dimensions are: 48 6 .
# A tibble: 48 x 6
   RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT  DEST 
          <dbl>       <dbl>    <dbl> <fct>    <fct> <fct>
 1        0.700    1.14e- 4   1.06   TRUE     AW    WL   
 2        0.636    5.55e- 3   1.56   TRUE     BT    AW   
 3        0.695    6.70e- 4   1.50   TRUE     BT    GH   
 4        0.794    1.01e- 4   1.52   FALSE    BT    HT   
 5        0.785    1.02e- 5   2.14   TRUE     BT    LB   
 6        0.756    9.45e-11   3.16   FALSE    BT    MI   
 7        0.794    1.77e- 4   1.56   FALSE    BT    NO   
 8        0.732    2.80e- 3   1.57   TRUE     BT    RT   
 9        0.723    8.76e- 3   0.921  FALSE    BT    WL   
10        0.776    1.49e- 4   2.25   TRUE     BT    ZB   
11        0.832    1.48e- 5   1.29   FALSE    CB    PL   
12        0.683    4.06e- 5   0.547  FALSE    CB    RC   
13        0.654    2.96e- 4   1.07   TRUE     GH    WL   
14        0.739    3.92e-12   2.79   TRUE     HN    CB   
15        0.829    0.         2.81   TRUE     HN    HT   
16        0.774    2.51e- 8   2.11   TRUE     HT    AW   
17        0.747    8.88e- 8   2.09   TRUE     HT    GH   
18        0.885    1.47e- 6   2.53   TRUE     HT    LB   
19        0.845    8.29e- 7   2.94   FALSE    HT    MI   
20        0.628    1.00e+ 0   0.0459 FALSE    HT    NO   
21        0.828    0.         2.74   TRUE     HT    PM   
22        0.695    1.60e- 8   2.23   TRUE     HT    RT   
23        0.639    3.73e- 6   1.55   FALSE    HT    WL   
24        0.869    0.         3.39   TRUE     HT    ZB   
25        0.738    1.26e- 7   1.94   FALSE    LB    CB   
26        0.726    8.69e- 6   1.50   TRUE     LB    MI   
27        0.748    0.         4.15   TRUE     MI    AW   
28        0.864    1.06e- 6   2.94   FALSE    MI    NO   
29        0.712    0.         3.38   TRUE     MI    OK   
30        0.786    0.         4.18   TRUE     MI    RT   
31        0.799    0.         3.49   TRUE     MI    ZB   
32        0.603    1.44e- 3   1.12   TRUE     RT    WL   
33        0.815    4.13e- 6   2.06   TRUE     SI    AD   
34        0.740    0.         4.01   TRUE     SI    AW   
35        0.748    6.55e-11   3.18   TRUE     SI    BT   
36        0.724    0.         3.18   TRUE     SI    CB   
37        0.789    0.         3.93   TRUE     SI    GH   
38        0.762    8.94e- 3   0.576  TRUE     SI    HN   
39        0.836    1.00e- 5   2.81   TRUE     SI    HT   
40        0.730    9.72e- 5   1.55   TRUE     SI    LB   
41        0.853    3.21e- 5   2.80   TRUE     SI    NO   
42        0.692    1.75e-12   3.17   TRUE     SI    OK   
43        0.835    0.         3.86   TRUE     SI    PL   
44        0.780    1.05e-10   2.54   TRUE     SI    PM   
45        0.691    1.44e-10   2.94   TRUE     SI    RC   
46        0.774    0.         4.05   TRUE     SI    RT   
47        0.789    0.         3.51   TRUE     SI    ZB   
48        0.817    4.24e- 7   2.77   TRUE     WL    ZB   

Modelling function received variables: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .

Modelling function received variables: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Getting Model Summary: 
Linear mixed model fit by maximum likelihood  ['lmerMod']
Formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) +      (1 | DEST)
   Data: data_item

     AIC      BIC   logLik deviance df.resid 
  -140.1   -127.0     77.0   -154.1       41 

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-1.90179 -0.46056 -0.08439  0.59392  2.02672 

Random effects:
 Groups   Name        Variance  Std.Dev.
 DEST     (Intercept) 2.077e-03 0.045572
 PORT     (Intercept) 9.894e-05 0.009947
 Residual             1.252e-03 0.035384
Number of obs: 48, groups:  DEST, 17; PORT, 11

Fixed effects:
              Estimate Std. Error t value
(Intercept)   0.722667   0.023240  31.096
B_HON_NOECO  -0.158283   0.044268  -3.576
PRED_ENV      0.025646   0.007243   3.541
ECO_DIFFTRUE -0.023867   0.016533  -1.444

Correlation of Fixed Effects:
            (Intr) B_HON_ PRED_E
B_HON_NOECO -0.321              
PRED_ENV    -0.621  0.352       
ECO_DIFFTRU -0.415  0.066 -0.189

Getting Model Coefficients from Summary: 
                Estimate  Std. Error   t value
(Intercept)   0.72266675 0.023239742 31.096161
B_HON_NOECO  -0.15828284 0.044268215 -3.575541
PRED_ENV      0.02564636 0.007242979  3.540858
ECO_DIFFTRUE -0.02386715 0.016533016 -1.443606

Getting Model ANOVA: 
Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + 
full_model:     (1 | DEST)
           Df     AIC     BIC logLik deviance  Chisq Chi Df Pr(>Chisq)   
null_model  6 -133.05 -121.82 72.525  -145.05                            
full_model  7 -140.07 -126.98 77.037  -154.07 9.0252      1   0.002663 **
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Plotting Model Coefficients: 

°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ 

Starting new analysis, with data index DIDX "3" and formula index FIDX "4" in Summary Tables.
Using formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 02_results_euk_otu99_deep_UNIF_model_data_2020-Jan-31-14-15-28_no_ph_with_hon_info.csv. 

Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.
- Setting types.
- Input dimensions are: 64 17 .
- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST VOY_FREQ B_FON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .
- Kept variables are: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST .
- Intermediate dimensions are: 64 6 .
- Undefined rows have been removed, assuming they were real "NA" and not "0".
- Final dimensions are: 48 6 .
# A tibble: 48 x 6
   RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT  DEST 
          <dbl>       <dbl>    <dbl> <fct>    <fct> <fct>
 1        0.708    1.14e- 4   1.06   TRUE     AW    WL   
 2        0.634    5.55e- 3   1.56   TRUE     BT    AW   
 3        0.708    6.70e- 4   1.50   TRUE     BT    GH   
 4        0.800    1.01e- 4   1.52   FALSE    BT    HT   
 5        0.791    1.02e- 5   2.14   TRUE     BT    LB   
 6        0.751    9.45e-11   3.16   FALSE    BT    MI   
 7        0.802    1.77e- 4   1.56   FALSE    BT    NO   
 8        0.732    2.80e- 3   1.57   TRUE     BT    RT   
 9        0.740    8.76e- 3   0.921  FALSE    BT    WL   
10        0.786    1.49e- 4   2.25   TRUE     BT    ZB   
11        0.831    1.48e- 5   1.29   FALSE    CB    PL   
12        0.688    4.06e- 5   0.547  FALSE    CB    RC   
13        0.671    2.96e- 4   1.07   TRUE     GH    WL   
14        0.736    3.92e-12   2.79   TRUE     HN    CB   
15        0.830    0.         2.81   TRUE     HN    HT   
16        0.781    2.51e- 8   2.11   TRUE     HT    AW   
17        0.753    8.88e- 8   2.09   TRUE     HT    GH   
18        0.892    1.47e- 6   2.53   TRUE     HT    LB   
19        0.851    8.29e- 7   2.94   FALSE    HT    MI   
20        0.635    1.00e+ 0   0.0459 FALSE    HT    NO   
21        0.824    0.         2.74   TRUE     HT    PM   
22        0.700    1.60e- 8   2.23   TRUE     HT    RT   
23        0.644    3.73e- 6   1.55   FALSE    HT    WL   
24        0.879    0.         3.39   TRUE     HT    ZB   
25        0.730    1.26e- 7   1.94   FALSE    LB    CB   
26        0.726    8.69e- 6   1.50   TRUE     LB    MI   
27        0.747    0.         4.15   TRUE     MI    AW   
28        0.870    1.06e- 6   2.94   FALSE    MI    NO   
29        0.715    0.         3.38   TRUE     MI    OK   
30        0.789    0.         4.18   TRUE     MI    RT   
31        0.802    0.         3.49   TRUE     MI    ZB   
32        0.607    1.44e- 3   1.12   TRUE     RT    WL   
33        0.815    4.13e- 6   2.06   TRUE     SI    AD   
34        0.737    0.         4.01   TRUE     SI    AW   
35        0.747    6.55e-11   3.18   TRUE     SI    BT   
36        0.724    0.         3.18   TRUE     SI    CB   
37        0.790    0.         3.93   TRUE     SI    GH   
38        0.768    8.94e- 3   0.576  TRUE     SI    HN   
39        0.837    1.00e- 5   2.81   TRUE     SI    HT   
40        0.732    9.72e- 5   1.55   TRUE     SI    LB   
41        0.851    3.21e- 5   2.80   TRUE     SI    NO   
42        0.698    1.75e-12   3.17   TRUE     SI    OK   
43        0.836    0.         3.86   TRUE     SI    PL   
44        0.780    1.05e-10   2.54   TRUE     SI    PM   
45        0.702    1.44e-10   2.94   TRUE     SI    RC   
46        0.774    0.         4.05   TRUE     SI    RT   
47        0.786    0.         3.51   TRUE     SI    ZB   
48        0.821    4.24e- 7   2.77   TRUE     WL    ZB   

Modelling function received variables: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .

Modelling function received variables: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Getting Model Summary: 
Linear mixed model fit by maximum likelihood  ['lmerMod']
Formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) +      (1 | DEST)
   Data: data_item

     AIC      BIC   logLik deviance df.resid 
  -137.7   -124.6     75.9   -151.7       41 

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-1.93942 -0.43001 -0.01406  0.52378  2.04698 

Random effects:
 Groups   Name        Variance  Std.Dev.
 DEST     (Intercept) 1.938e-03 0.044026
 PORT     (Intercept) 9.361e-05 0.009675
 Residual             1.385e-03 0.037217
Number of obs: 48, groups:  DEST, 17; PORT, 11

Fixed effects:
              Estimate Std. Error t value
(Intercept)   0.728026   0.023645  30.790
B_HON_NOECO  -0.155901   0.046259  -3.370
PRED_ENV      0.024451   0.007484   3.267
ECO_DIFFTRUE -0.023820   0.017184  -1.386

Correlation of Fixed Effects:
            (Intr) B_HON_ PRED_E
B_HON_NOECO -0.326              
PRED_ENV    -0.629  0.347       
ECO_DIFFTRU -0.417  0.068 -0.199

Getting Model Coefficients from Summary: 
                Estimate  Std. Error   t value
(Intercept)   0.72802610 0.023645089 30.789739
B_HON_NOECO  -0.15590069 0.046259288 -3.370149
PRED_ENV      0.02445149 0.007484109  3.267120
ECO_DIFFTRUE -0.02381975 0.017184426 -1.386124

Getting Model ANOVA: 
Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + 
full_model:     (1 | DEST)
           Df     AIC     BIC logLik deviance  Chisq Chi Df Pr(>Chisq)   
null_model  6 -131.56 -120.33 71.780  -143.56                            
full_model  7 -137.73 -124.63 75.865  -151.73 8.1705      1   0.004258 **
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Plotting Model Coefficients: 

°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ 

Starting new analysis, with data index DIDX "4" and formula index FIDX "4" in Summary Tables.
Using formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 02_results_euk_otu99_deep_UNIF_model_data_2020-Jan-31-14-15-28_with_hon_info.csv. 

Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.
- Setting types.
- Input dimensions are: 69 17 .
- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST VOY_FREQ B_FON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .
- Kept variables are: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST .
- Intermediate dimensions are: 69 6 .
- Undefined rows have been removed, assuming they were real "NA" and not "0".
- Final dimensions are: 48 6 .
# A tibble: 48 x 6
   RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT  DEST 
          <dbl>       <dbl>    <dbl> <fct>    <fct> <fct>
 1        0.708    1.14e- 4   1.06   TRUE     AW    WL   
 2        0.634    5.55e- 3   1.56   TRUE     BT    AW   
 3        0.708    6.70e- 4   1.50   TRUE     BT    GH   
 4        0.800    1.01e- 4   1.52   FALSE    BT    HT   
 5        0.791    1.02e- 5   2.14   TRUE     BT    LB   
 6        0.751    9.45e-11   3.16   FALSE    BT    MI   
 7        0.802    1.77e- 4   1.56   FALSE    BT    NO   
 8        0.732    2.80e- 3   1.57   TRUE     BT    RT   
 9        0.740    8.76e- 3   0.921  FALSE    BT    WL   
10        0.786    1.49e- 4   2.25   TRUE     BT    ZB   
11        0.831    1.48e- 5   1.29   FALSE    CB    PL   
12        0.688    4.06e- 5   0.547  FALSE    CB    RC   
13        0.671    2.96e- 4   1.07   TRUE     GH    WL   
14        0.736    3.92e-12   2.79   TRUE     HN    CB   
15        0.830    0.         2.81   TRUE     HN    HT   
16        0.781    2.51e- 8   2.11   TRUE     HT    AW   
17        0.753    8.88e- 8   2.09   TRUE     HT    GH   
18        0.892    1.47e- 6   2.53   TRUE     HT    LB   
19        0.851    8.29e- 7   2.94   FALSE    HT    MI   
20        0.635    1.00e+ 0   0.0459 FALSE    HT    NO   
21        0.824    0.         2.74   TRUE     HT    PM   
22        0.700    1.60e- 8   2.23   TRUE     HT    RT   
23        0.644    3.73e- 6   1.55   FALSE    HT    WL   
24        0.879    0.         3.39   TRUE     HT    ZB   
25        0.730    1.26e- 7   1.94   FALSE    LB    CB   
26        0.726    8.69e- 6   1.50   TRUE     LB    MI   
27        0.747    0.         4.15   TRUE     MI    AW   
28        0.870    1.06e- 6   2.94   FALSE    MI    NO   
29        0.715    0.         3.38   TRUE     MI    OK   
30        0.789    0.         4.18   TRUE     MI    RT   
31        0.802    0.         3.49   TRUE     MI    ZB   
32        0.607    1.44e- 3   1.12   TRUE     RT    WL   
33        0.815    4.13e- 6   2.06   TRUE     SI    AD   
34        0.737    0.         4.01   TRUE     SI    AW   
35        0.747    6.55e-11   3.18   TRUE     SI    BT   
36        0.724    0.         3.18   TRUE     SI    CB   
37        0.790    0.         3.93   TRUE     SI    GH   
38        0.768    8.94e- 3   0.576  TRUE     SI    HN   
39        0.837    1.00e- 5   2.81   TRUE     SI    HT   
40        0.732    9.72e- 5   1.55   TRUE     SI    LB   
41        0.851    3.21e- 5   2.80   TRUE     SI    NO   
42        0.698    1.75e-12   3.17   TRUE     SI    OK   
43        0.836    0.         3.86   TRUE     SI    PL   
44        0.780    1.05e-10   2.54   TRUE     SI    PM   
45        0.702    1.44e-10   2.94   TRUE     SI    RC   
46        0.774    0.         4.05   TRUE     SI    RT   
47        0.786    0.         3.51   TRUE     SI    ZB   
48        0.821    4.24e- 7   2.77   TRUE     WL    ZB   

Modelling function received variables: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .

Modelling function received variables: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Getting Model Summary: 
Linear mixed model fit by maximum likelihood  ['lmerMod']
Formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) +      (1 | DEST)
   Data: data_item

     AIC      BIC   logLik deviance df.resid 
  -137.7   -124.6     75.9   -151.7       41 

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-1.93942 -0.43001 -0.01406  0.52378  2.04698 

Random effects:
 Groups   Name        Variance  Std.Dev.
 DEST     (Intercept) 1.938e-03 0.044026
 PORT     (Intercept) 9.361e-05 0.009675
 Residual             1.385e-03 0.037217
Number of obs: 48, groups:  DEST, 17; PORT, 11

Fixed effects:
              Estimate Std. Error t value
(Intercept)   0.728026   0.023645  30.790
B_HON_NOECO  -0.155901   0.046259  -3.370
PRED_ENV      0.024451   0.007484   3.267
ECO_DIFFTRUE -0.023820   0.017184  -1.386

Correlation of Fixed Effects:
            (Intr) B_HON_ PRED_E
B_HON_NOECO -0.326              
PRED_ENV    -0.629  0.347       
ECO_DIFFTRU -0.417  0.068 -0.199

Getting Model Coefficients from Summary: 
                Estimate  Std. Error   t value
(Intercept)   0.72802610 0.023645089 30.789739
B_HON_NOECO  -0.15590069 0.046259288 -3.370149
PRED_ENV      0.02445149 0.007484109  3.267120
ECO_DIFFTRUE -0.02381975 0.017184426 -1.386124

Getting Model ANOVA: 
Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + 
full_model:     (1 | DEST)
           Df     AIC     BIC logLik deviance  Chisq Chi Df Pr(>Chisq)   
null_model  6 -131.56 -120.33 71.780  -143.56                            
full_model  7 -137.73 -124.63 75.865  -151.73 8.1705      1   0.004258 **
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Plotting Model Coefficients: 

°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ 

Starting new analysis, with data index DIDX "5" and formula index FIDX "4" in Summary Tables.
Using formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 03_results_euk_asv00_deep_JAQU_model_data_2020-Jan-31-14-15-41_no_ph_with_hon_info.csv. 

Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.
- Setting types.
- Input dimensions are: 64 17 .
- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST VOY_FREQ B_FON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .
- Kept variables are: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST .
- Intermediate dimensions are: 64 6 .
- Undefined rows have been removed, assuming they were real "NA" and not "0".
- Final dimensions are: 48 6 .
# A tibble: 48 x 6
   RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT  DEST 
          <dbl>       <dbl>    <dbl> <fct>    <fct> <fct>
 1        0.950    1.14e- 4   1.06   TRUE     AW    WL   
 2        0.902    5.55e- 3   1.56   TRUE     BT    AW   
 3        0.942    6.70e- 4   1.50   TRUE     BT    GH   
 4        0.989    1.01e- 4   1.52   FALSE    BT    HT   
 5        0.964    1.02e- 5   2.14   TRUE     BT    LB   
 6        0.974    9.45e-11   3.16   FALSE    BT    MI   
 7        0.985    1.77e- 4   1.56   FALSE    BT    NO   
 8        0.955    2.80e- 3   1.57   TRUE     BT    RT   
 9        0.958    8.76e- 3   0.921  FALSE    BT    WL   
10        0.955    1.49e- 4   2.25   TRUE     BT    ZB   
11        1        1.48e- 5   1.29   FALSE    CB    PL   
12        0.908    4.06e- 5   0.547  FALSE    CB    RC   
13        0.913    2.96e- 4   1.07   TRUE     GH    WL   
14        0.980    3.92e-12   2.79   TRUE     HN    CB   
15        0.993    0.         2.81   TRUE     HN    HT   
16        0.988    2.51e- 8   2.11   TRUE     HT    AW   
17        0.969    8.88e- 8   2.09   TRUE     HT    GH   
18        1.00     1.47e- 6   2.53   TRUE     HT    LB   
19        0.994    8.29e- 7   2.94   FALSE    HT    MI   
20        0.892    1.00e+ 0   0.0459 FALSE    HT    NO   
21        0.997    0.         2.74   TRUE     HT    PM   
22        0.948    1.60e- 8   2.23   TRUE     HT    RT   
23        0.906    3.73e- 6   1.55   FALSE    HT    WL   
24        0.999    0.         3.39   TRUE     HT    ZB   
25        0.940    1.26e- 7   1.94   FALSE    LB    CB   
26        0.943    8.69e- 6   1.50   TRUE     LB    MI   
27        0.988    0.         4.15   TRUE     MI    AW   
28        0.998    1.06e- 6   2.94   FALSE    MI    NO   
29        0.963    0.         3.38   TRUE     MI    OK   
30        0.991    0.         4.18   TRUE     MI    RT   
31        0.988    0.         3.49   TRUE     MI    ZB   
32        0.894    1.44e- 3   1.12   TRUE     RT    WL   
33        0.971    4.13e- 6   2.06   TRUE     SI    AD   
34        0.985    0.         4.01   TRUE     SI    AW   
35        0.973    6.55e-11   3.18   TRUE     SI    BT   
36        0.981    0.         3.18   TRUE     SI    CB   
37        0.995    0.         3.93   TRUE     SI    GH   
38        0.959    8.94e- 3   0.576  TRUE     SI    HN   
39        0.997    1.00e- 5   2.81   TRUE     SI    HT   
40        0.967    9.72e- 5   1.55   TRUE     SI    LB   
41        0.997    3.21e- 5   2.80   TRUE     SI    NO   
42        0.958    1.75e-12   3.17   TRUE     SI    OK   
43        0.998    0.         3.86   TRUE     SI    PL   
44        0.996    1.05e-10   2.54   TRUE     SI    PM   
45        0.965    1.44e-10   2.94   TRUE     SI    RC   
46        0.992    0.         4.05   TRUE     SI    RT   
47        0.984    0.         3.51   TRUE     SI    ZB   
48        0.996    4.24e- 7   2.77   TRUE     WL    ZB   

Modelling function received variables: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Modelling function received variables: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Getting Model Summary: 
Linear mixed model fit by maximum likelihood  ['lmerMod']
Formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) +      (1 | DEST)
   Data: data_item

     AIC      BIC   logLik deviance df.resid 
  -221.3   -208.2    117.7   -235.3       41 

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-2.38107 -0.46947  0.01774  0.58778  1.81593 

Random effects:
 Groups   Name        Variance  Std.Dev.
 DEST     (Intercept) 0.0001561 0.01249 
 PORT     (Intercept) 0.0000000 0.00000 
 Residual             0.0003247 0.01802 
Number of obs: 48, groups:  DEST, 17; PORT, 11

Fixed effects:
              Estimate Std. Error t value
(Intercept)   0.934088   0.009349  99.913
B_HON_NOECO  -0.054300   0.021217  -2.559
PRED_ENV      0.017608   0.003198   5.507
ECO_DIFFTRUE -0.007589   0.007592  -1.000

Correlation of Fixed Effects:
            (Intr) B_HON_ PRED_E
B_HON_NOECO -0.351              
PRED_ENV    -0.654  0.313       
ECO_DIFFTRU -0.407  0.085 -0.280
convergence code: 0
boundary (singular) fit: see ?isSingular

Getting Model Coefficients from Summary: 
                 Estimate  Std. Error    t value
(Intercept)   0.934087851 0.009349048 99.9126184
B_HON_NOECO  -0.054300291 0.021216761 -2.5593110
PRED_ENV      0.017608080 0.003197524  5.5067863
ECO_DIFFTRUE -0.007588602 0.007592272 -0.9995166

Getting Model ANOVA: 
Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + 
full_model:     (1 | DEST)
           Df     AIC     BIC logLik deviance  Chisq Chi Df Pr(>Chisq)  
null_model  6 -217.82 -206.59 114.91  -229.82                           
full_model  7 -221.34 -208.25 117.67  -235.34 5.5251      1    0.01875 *
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Plotting Model Coefficients: 
boundary (singular) fit: see ?isSingular

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Starting new analysis, with data index DIDX "6" and formula index FIDX "4" in Summary Tables.
Using formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 03_results_euk_asv00_deep_JAQU_model_data_2020-Jan-31-14-15-41_with_hon_info.csv. 

Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.
- Setting types.
- Input dimensions are: 69 17 .
- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST VOY_FREQ B_FON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .
- Kept variables are: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST .
- Intermediate dimensions are: 69 6 .
- Undefined rows have been removed, assuming they were real "NA" and not "0".
- Final dimensions are: 48 6 .
# A tibble: 48 x 6
   RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT  DEST 
          <dbl>       <dbl>    <dbl> <fct>    <fct> <fct>
 1        0.950    1.14e- 4   1.06   TRUE     AW    WL   
 2        0.902    5.55e- 3   1.56   TRUE     BT    AW   
 3        0.942    6.70e- 4   1.50   TRUE     BT    GH   
 4        0.989    1.01e- 4   1.52   FALSE    BT    HT   
 5        0.964    1.02e- 5   2.14   TRUE     BT    LB   
 6        0.974    9.45e-11   3.16   FALSE    BT    MI   
 7        0.985    1.77e- 4   1.56   FALSE    BT    NO   
 8        0.955    2.80e- 3   1.57   TRUE     BT    RT   
 9        0.958    8.76e- 3   0.921  FALSE    BT    WL   
10        0.955    1.49e- 4   2.25   TRUE     BT    ZB   
11        1        1.48e- 5   1.29   FALSE    CB    PL   
12        0.908    4.06e- 5   0.547  FALSE    CB    RC   
13        0.913    2.96e- 4   1.07   TRUE     GH    WL   
14        0.980    3.92e-12   2.79   TRUE     HN    CB   
15        0.993    0.         2.81   TRUE     HN    HT   
16        0.988    2.51e- 8   2.11   TRUE     HT    AW   
17        0.969    8.88e- 8   2.09   TRUE     HT    GH   
18        1.00     1.47e- 6   2.53   TRUE     HT    LB   
19        0.994    8.29e- 7   2.94   FALSE    HT    MI   
20        0.892    1.00e+ 0   0.0459 FALSE    HT    NO   
21        0.997    0.         2.74   TRUE     HT    PM   
22        0.948    1.60e- 8   2.23   TRUE     HT    RT   
23        0.906    3.73e- 6   1.55   FALSE    HT    WL   
24        0.999    0.         3.39   TRUE     HT    ZB   
25        0.940    1.26e- 7   1.94   FALSE    LB    CB   
26        0.943    8.69e- 6   1.50   TRUE     LB    MI   
27        0.988    0.         4.15   TRUE     MI    AW   
28        0.998    1.06e- 6   2.94   FALSE    MI    NO   
29        0.963    0.         3.38   TRUE     MI    OK   
30        0.991    0.         4.18   TRUE     MI    RT   
31        0.988    0.         3.49   TRUE     MI    ZB   
32        0.894    1.44e- 3   1.12   TRUE     RT    WL   
33        0.971    4.13e- 6   2.06   TRUE     SI    AD   
34        0.985    0.         4.01   TRUE     SI    AW   
35        0.973    6.55e-11   3.18   TRUE     SI    BT   
36        0.981    0.         3.18   TRUE     SI    CB   
37        0.995    0.         3.93   TRUE     SI    GH   
38        0.959    8.94e- 3   0.576  TRUE     SI    HN   
39        0.997    1.00e- 5   2.81   TRUE     SI    HT   
40        0.967    9.72e- 5   1.55   TRUE     SI    LB   
41        0.997    3.21e- 5   2.80   TRUE     SI    NO   
42        0.958    1.75e-12   3.17   TRUE     SI    OK   
43        0.998    0.         3.86   TRUE     SI    PL   
44        0.996    1.05e-10   2.54   TRUE     SI    PM   
45        0.965    1.44e-10   2.94   TRUE     SI    RC   
46        0.992    0.         4.05   TRUE     SI    RT   
47        0.984    0.         3.51   TRUE     SI    ZB   
48        0.996    4.24e- 7   2.77   TRUE     WL    ZB   

Modelling function received variables: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Modelling function received variables: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Getting Model Summary: 
Linear mixed model fit by maximum likelihood  ['lmerMod']
Formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) +      (1 | DEST)
   Data: data_item

     AIC      BIC   logLik deviance df.resid 
  -221.3   -208.2    117.7   -235.3       41 

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-2.38107 -0.46947  0.01774  0.58778  1.81593 

Random effects:
 Groups   Name        Variance  Std.Dev.
 DEST     (Intercept) 0.0001561 0.01249 
 PORT     (Intercept) 0.0000000 0.00000 
 Residual             0.0003247 0.01802 
Number of obs: 48, groups:  DEST, 17; PORT, 11

Fixed effects:
              Estimate Std. Error t value
(Intercept)   0.934088   0.009349  99.913
B_HON_NOECO  -0.054300   0.021217  -2.559
PRED_ENV      0.017608   0.003198   5.507
ECO_DIFFTRUE -0.007589   0.007592  -1.000

Correlation of Fixed Effects:
            (Intr) B_HON_ PRED_E
B_HON_NOECO -0.351              
PRED_ENV    -0.654  0.313       
ECO_DIFFTRU -0.407  0.085 -0.280
convergence code: 0
boundary (singular) fit: see ?isSingular

Getting Model Coefficients from Summary: 
                 Estimate  Std. Error    t value
(Intercept)   0.934087851 0.009349048 99.9126184
B_HON_NOECO  -0.054300291 0.021216761 -2.5593110
PRED_ENV      0.017608080 0.003197524  5.5067863
ECO_DIFFTRUE -0.007588602 0.007592272 -0.9995166

Getting Model ANOVA: 
Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + 
full_model:     (1 | DEST)
           Df     AIC     BIC logLik deviance  Chisq Chi Df Pr(>Chisq)  
null_model  6 -217.82 -206.59 114.91  -229.82                           
full_model  7 -221.34 -208.25 117.67  -235.34 5.5251      1    0.01875 *
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Plotting Model Coefficients: 
boundary (singular) fit: see ?isSingular

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Starting new analysis, with data index DIDX "7" and formula index FIDX "4" in Summary Tables.
Using formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 04_results_euk_otu99_deep_JAQU_model_data_2020-Jan-31-14-15-52_no_ph_with_hon_info.csv. 

Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.
- Setting types.
- Input dimensions are: 64 17 .
- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST VOY_FREQ B_FON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .
- Kept variables are: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST .
- Intermediate dimensions are: 64 6 .
- Undefined rows have been removed, assuming they were real "NA" and not "0".
- Final dimensions are: 48 6 .
# A tibble: 48 x 6
   RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT  DEST 
          <dbl>       <dbl>    <dbl> <fct>    <fct> <fct>
 1        0.919    1.14e- 4   1.06   TRUE     AW    WL   
 2        0.848    5.55e- 3   1.56   TRUE     BT    AW   
 3        0.907    6.70e- 4   1.50   TRUE     BT    GH   
 4        0.980    1.01e- 4   1.52   FALSE    BT    HT   
 5        0.938    1.02e- 5   2.14   TRUE     BT    LB   
 6        0.934    9.45e-11   3.16   FALSE    BT    MI   
 7        0.971    1.77e- 4   1.56   FALSE    BT    NO   
 8        0.928    2.80e- 3   1.57   TRUE     BT    RT   
 9        0.935    8.76e- 3   0.921  FALSE    BT    WL   
10        0.932    1.49e- 4   2.25   TRUE     BT    ZB   
11        0.998    1.48e- 5   1.29   FALSE    CB    PL   
12        0.863    4.06e- 5   0.547  FALSE    CB    RC   
13        0.879    2.96e- 4   1.07   TRUE     GH    WL   
14        0.948    3.92e-12   2.79   TRUE     HN    CB   
15        0.988    0.         2.81   TRUE     HN    HT   
16        0.981    2.51e- 8   2.11   TRUE     HT    AW   
17        0.951    8.88e- 8   2.09   TRUE     HT    GH   
18        0.998    1.47e- 6   2.53   TRUE     HT    LB   
19        0.987    8.29e- 7   2.94   FALSE    HT    MI   
20        0.848    1.00e+ 0   0.0459 FALSE    HT    NO   
21        0.995    0.         2.74   TRUE     HT    PM   
22        0.921    1.60e- 8   2.23   TRUE     HT    RT   
23        0.866    3.73e- 6   1.55   FALSE    HT    WL   
24        0.997    0.         3.39   TRUE     HT    ZB   
25        0.902    1.26e- 7   1.94   FALSE    LB    CB   
26        0.908    8.69e- 6   1.50   TRUE     LB    MI   
27        0.967    0.         4.15   TRUE     MI    AW   
28        0.990    1.06e- 6   2.94   FALSE    MI    NO   
29        0.928    0.         3.38   TRUE     MI    OK   
30        0.977    0.         4.18   TRUE     MI    RT   
31        0.966    0.         3.49   TRUE     MI    ZB   
32        0.840    1.44e- 3   1.12   TRUE     RT    WL   
33        0.946    4.13e- 6   2.06   TRUE     SI    AD   
34        0.960    0.         4.01   TRUE     SI    AW   
35        0.947    6.55e-11   3.18   TRUE     SI    BT   
36        0.950    0.         3.18   TRUE     SI    CB   
37        0.986    0.         3.93   TRUE     SI    GH   
38        0.935    8.94e- 3   0.576  TRUE     SI    HN   
39        0.993    1.00e- 5   2.81   TRUE     SI    HT   
40        0.932    9.72e- 5   1.55   TRUE     SI    LB   
41        0.994    3.21e- 5   2.80   TRUE     SI    NO   
42        0.922    1.75e-12   3.17   TRUE     SI    OK   
43        0.996    0.         3.86   TRUE     SI    PL   
44        0.983    1.05e-10   2.54   TRUE     SI    PM   
45        0.930    1.44e-10   2.94   TRUE     SI    RC   
46        0.976    0.         4.05   TRUE     SI    RT   
47        0.952    0.         3.51   TRUE     SI    ZB   
48        0.987    4.24e- 7   2.77   TRUE     WL    ZB   

Modelling function received variables: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Modelling function received variables: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Getting Model Summary: 
Linear mixed model fit by maximum likelihood  ['lmerMod']
Formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) +      (1 | DEST)
   Data: data_item

     AIC      BIC   logLik deviance df.resid 
  -180.2   -167.1     97.1   -194.2       41 

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.3856 -0.4993 -0.0156  0.5616  2.0317 

Random effects:
 Groups   Name        Variance  Std.Dev. 
 DEST     (Intercept) 4.228e-04 2.056e-02
 PORT     (Intercept) 7.410e-14 2.722e-07
 Residual             7.388e-04 2.718e-02
Number of obs: 48, groups:  DEST, 17; PORT, 11

Fixed effects:
              Estimate Std. Error t value
(Intercept)   0.902684   0.014372  62.808
B_HON_NOECO  -0.075333   0.032178  -2.341
PRED_ENV      0.022661   0.004874   4.649
ECO_DIFFTRUE -0.011357   0.011562  -0.982

Correlation of Fixed Effects:
            (Intr) B_HON_ PRED_E
B_HON_NOECO -0.347              
PRED_ENV    -0.649  0.316       
ECO_DIFFTRU -0.405  0.080 -0.278
convergence code: 0
boundary (singular) fit: see ?isSingular

Getting Model Coefficients from Summary: 
                Estimate  Std. Error    t value
(Intercept)   0.90268428 0.014372132 62.8079578
B_HON_NOECO  -0.07533303 0.032178010 -2.3411340
PRED_ENV      0.02266067 0.004873954  4.6493396
ECO_DIFFTRUE -0.01135716 0.011562456 -0.9822449

Getting Model ANOVA: 
Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + 
full_model:     (1 | DEST)
           Df     AIC     BIC logLik deviance  Chisq Chi Df Pr(>Chisq)  
null_model  6 -177.49 -166.26 94.743  -189.49                           
full_model  7 -180.21 -167.11 97.103  -194.21 4.7189      1    0.02983 *
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Plotting Model Coefficients: 
boundary (singular) fit: see ?isSingular

°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ 

Starting new analysis, with data index DIDX "8" and formula index FIDX "4" in Summary Tables.
Using formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 04_results_euk_otu99_deep_JAQU_model_data_2020-Jan-31-14-15-52_with_hon_info.csv. 

Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.
- Setting types.
- Input dimensions are: 69 17 .
- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST VOY_FREQ B_FON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .
- Kept variables are: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST .
- Intermediate dimensions are: 69 6 .
- Undefined rows have been removed, assuming they were real "NA" and not "0".
- Final dimensions are: 48 6 .
# A tibble: 48 x 6
   RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT  DEST 
          <dbl>       <dbl>    <dbl> <fct>    <fct> <fct>
 1        0.919    1.14e- 4   1.06   TRUE     AW    WL   
 2        0.848    5.55e- 3   1.56   TRUE     BT    AW   
 3        0.907    6.70e- 4   1.50   TRUE     BT    GH   
 4        0.980    1.01e- 4   1.52   FALSE    BT    HT   
 5        0.938    1.02e- 5   2.14   TRUE     BT    LB   
 6        0.934    9.45e-11   3.16   FALSE    BT    MI   
 7        0.971    1.77e- 4   1.56   FALSE    BT    NO   
 8        0.928    2.80e- 3   1.57   TRUE     BT    RT   
 9        0.935    8.76e- 3   0.921  FALSE    BT    WL   
10        0.932    1.49e- 4   2.25   TRUE     BT    ZB   
11        0.998    1.48e- 5   1.29   FALSE    CB    PL   
12        0.863    4.06e- 5   0.547  FALSE    CB    RC   
13        0.879    2.96e- 4   1.07   TRUE     GH    WL   
14        0.948    3.92e-12   2.79   TRUE     HN    CB   
15        0.988    0.         2.81   TRUE     HN    HT   
16        0.981    2.51e- 8   2.11   TRUE     HT    AW   
17        0.951    8.88e- 8   2.09   TRUE     HT    GH   
18        0.998    1.47e- 6   2.53   TRUE     HT    LB   
19        0.987    8.29e- 7   2.94   FALSE    HT    MI   
20        0.848    1.00e+ 0   0.0459 FALSE    HT    NO   
21        0.995    0.         2.74   TRUE     HT    PM   
22        0.921    1.60e- 8   2.23   TRUE     HT    RT   
23        0.866    3.73e- 6   1.55   FALSE    HT    WL   
24        0.997    0.         3.39   TRUE     HT    ZB   
25        0.902    1.26e- 7   1.94   FALSE    LB    CB   
26        0.908    8.69e- 6   1.50   TRUE     LB    MI   
27        0.967    0.         4.15   TRUE     MI    AW   
28        0.990    1.06e- 6   2.94   FALSE    MI    NO   
29        0.928    0.         3.38   TRUE     MI    OK   
30        0.977    0.         4.18   TRUE     MI    RT   
31        0.966    0.         3.49   TRUE     MI    ZB   
32        0.840    1.44e- 3   1.12   TRUE     RT    WL   
33        0.946    4.13e- 6   2.06   TRUE     SI    AD   
34        0.960    0.         4.01   TRUE     SI    AW   
35        0.947    6.55e-11   3.18   TRUE     SI    BT   
36        0.950    0.         3.18   TRUE     SI    CB   
37        0.986    0.         3.93   TRUE     SI    GH   
38        0.935    8.94e- 3   0.576  TRUE     SI    HN   
39        0.993    1.00e- 5   2.81   TRUE     SI    HT   
40        0.932    9.72e- 5   1.55   TRUE     SI    LB   
41        0.994    3.21e- 5   2.80   TRUE     SI    NO   
42        0.922    1.75e-12   3.17   TRUE     SI    OK   
43        0.996    0.         3.86   TRUE     SI    PL   
44        0.983    1.05e-10   2.54   TRUE     SI    PM   
45        0.930    1.44e-10   2.94   TRUE     SI    RC   
46        0.976    0.         4.05   TRUE     SI    RT   
47        0.952    0.         3.51   TRUE     SI    ZB   
48        0.987    4.24e- 7   2.77   TRUE     WL    ZB   

Modelling function received variables: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Modelling function received variables: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Getting Model Summary: 
Linear mixed model fit by maximum likelihood  ['lmerMod']
Formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) +      (1 | DEST)
   Data: data_item

     AIC      BIC   logLik deviance df.resid 
  -180.2   -167.1     97.1   -194.2       41 

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.3856 -0.4993 -0.0156  0.5616  2.0317 

Random effects:
 Groups   Name        Variance  Std.Dev. 
 DEST     (Intercept) 4.228e-04 2.056e-02
 PORT     (Intercept) 7.410e-14 2.722e-07
 Residual             7.388e-04 2.718e-02
Number of obs: 48, groups:  DEST, 17; PORT, 11

Fixed effects:
              Estimate Std. Error t value
(Intercept)   0.902684   0.014372  62.808
B_HON_NOECO  -0.075333   0.032178  -2.341
PRED_ENV      0.022661   0.004874   4.649
ECO_DIFFTRUE -0.011357   0.011562  -0.982

Correlation of Fixed Effects:
            (Intr) B_HON_ PRED_E
B_HON_NOECO -0.347              
PRED_ENV    -0.649  0.316       
ECO_DIFFTRU -0.405  0.080 -0.278
convergence code: 0
boundary (singular) fit: see ?isSingular

Getting Model Coefficients from Summary: 
                Estimate  Std. Error    t value
(Intercept)   0.90268428 0.014372132 62.8079578
B_HON_NOECO  -0.07533303 0.032178010 -2.3411340
PRED_ENV      0.02266067 0.004873954  4.6493396
ECO_DIFFTRUE -0.01135716 0.011562456 -0.9822449

Getting Model ANOVA: 
Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + 
full_model:     (1 | DEST)
           Df     AIC     BIC logLik deviance  Chisq Chi Df Pr(>Chisq)  
null_model  6 -177.49 -166.26 94.743  -189.49                           
full_model  7 -180.21 -167.11 97.103  -194.21 4.7189      1    0.02983 *
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Plotting Model Coefficients: 
boundary (singular) fit: see ?isSingular

°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ 

Starting new analysis, with data index DIDX "9" and formula index FIDX "4" in Summary Tables.
Using formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 05_results_euk_asv00_shal_UNIF_model_data_2020-Jan-31-14-16-03_no_ph_with_hon_info.csv. 

Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.
- Setting types.
- Input dimensions are: 64 17 .
- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST VOY_FREQ B_FON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .
- Kept variables are: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST .
- Intermediate dimensions are: 64 6 .
- Undefined rows have been removed, assuming they were real "NA" and not "0".
- Final dimensions are: 48 6 .
# A tibble: 48 x 6
   RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT  DEST 
          <dbl>       <dbl>    <dbl> <fct>    <fct> <fct>
 1        0.702    1.14e- 4   1.06   TRUE     AW    WL   
 2        0.634    5.55e- 3   1.56   TRUE     BT    AW   
 3        0.713    6.70e- 4   1.50   TRUE     BT    GH   
 4        0.800    1.01e- 4   1.52   FALSE    BT    HT   
 5        0.782    1.02e- 5   2.14   TRUE     BT    LB   
 6        0.760    9.45e-11   3.16   FALSE    BT    MI   
 7        0.801    1.77e- 4   1.56   FALSE    BT    NO   
 8        0.728    2.80e- 3   1.57   TRUE     BT    RT   
 9        0.728    8.76e- 3   0.921  FALSE    BT    WL   
10        0.764    1.49e- 4   2.25   TRUE     BT    ZB   
11        0.836    1.48e- 5   1.29   FALSE    CB    PL   
12        0.683    4.06e- 5   0.547  FALSE    CB    RC   
13        0.673    2.96e- 4   1.07   TRUE     GH    WL   
14        0.741    3.92e-12   2.79   TRUE     HN    CB   
15        0.840    0.         2.81   TRUE     HN    HT   
16        0.779    2.51e- 8   2.11   TRUE     HT    AW   
17        0.765    8.88e- 8   2.09   TRUE     HT    GH   
18        0.889    1.47e- 6   2.53   TRUE     HT    LB   
19        0.849    8.29e- 7   2.94   FALSE    HT    MI   
20        0.632    1.00e+ 0   0.0459 FALSE    HT    NO   
21        0.838    0.         2.74   TRUE     HT    PM   
22        0.701    1.60e- 8   2.23   TRUE     HT    RT   
23        0.653    3.73e- 6   1.55   FALSE    HT    WL   
24        0.874    0.         3.39   TRUE     HT    ZB   
25        0.735    1.26e- 7   1.94   FALSE    LB    CB   
26        0.721    8.69e- 6   1.50   TRUE     LB    MI   
27        0.745    0.         4.15   TRUE     MI    AW   
28        0.869    1.06e- 6   2.94   FALSE    MI    NO   
29        0.719    0.         3.38   TRUE     MI    OK   
30        0.785    0.         4.18   TRUE     MI    RT   
31        0.790    0.         3.49   TRUE     MI    ZB   
32        0.603    1.44e- 3   1.12   TRUE     RT    WL   
33        0.796    4.13e- 6   2.06   TRUE     SI    AD   
34        0.741    0.         4.01   TRUE     SI    AW   
35        0.743    6.55e-11   3.18   TRUE     SI    BT   
36        0.737    0.         3.18   TRUE     SI    CB   
37        0.797    0.         3.93   TRUE     SI    GH   
38        0.761    8.94e- 3   0.576  TRUE     SI    HN   
39        0.842    1.00e- 5   2.81   TRUE     SI    HT   
40        0.727    9.72e- 5   1.55   TRUE     SI    LB   
41        0.858    3.21e- 5   2.80   TRUE     SI    NO   
42        0.706    1.75e-12   3.17   TRUE     SI    OK   
43        0.841    0.         3.86   TRUE     SI    PL   
44        0.781    1.05e-10   2.54   TRUE     SI    PM   
45        0.699    1.44e-10   2.94   TRUE     SI    RC   
46        0.777    0.         4.05   TRUE     SI    RT   
47        0.777    0.         3.51   TRUE     SI    ZB   
48        0.813    4.24e- 7   2.77   TRUE     WL    ZB   

Modelling function received variables: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .

Modelling function received variables: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .

Getting Model Summary: 
Linear mixed model fit by maximum likelihood  ['lmerMod']
Formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) +      (1 | DEST)
   Data: data_item

     AIC      BIC   logLik deviance df.resid 
  -140.5   -127.4     77.2   -154.5       41 

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-1.9523 -0.3791 -0.1032  0.5563  1.9589 

Random effects:
 Groups   Name        Variance  Std.Dev.
 DEST     (Intercept) 0.0019538 0.04420 
 PORT     (Intercept) 0.0002001 0.01415 
 Residual             0.0012038 0.03470 
Number of obs: 48, groups:  DEST, 17; PORT, 11

Fixed effects:
              Estimate Std. Error t value
(Intercept)   0.721702   0.023949  30.135
B_HON_NOECO  -0.166182   0.044067  -3.771
PRED_ENV      0.027017   0.007465   3.619
ECO_DIFFTRUE -0.023079   0.016662  -1.385

Correlation of Fixed Effects:
            (Intr) B_HON_ PRED_E
B_HON_NOECO -0.329              
PRED_ENV    -0.634  0.363       
ECO_DIFFTRU -0.439  0.079 -0.142

Getting Model Coefficients from Summary: 
                Estimate  Std. Error   t value
(Intercept)   0.72170214 0.023949065 30.134877
B_HON_NOECO  -0.16618164 0.044067231 -3.771093
PRED_ENV      0.02701726 0.007465388  3.619003
ECO_DIFFTRUE -0.02307903 0.016662276 -1.385107

Getting Model ANOVA: 
Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + 
full_model:     (1 | DEST)
           Df     AIC     BIC logLik deviance  Chisq Chi Df Pr(>Chisq)   
null_model  6 -133.12 -121.90 72.563  -145.12                            
full_model  7 -140.47 -127.37 77.236  -154.47 9.3471      1   0.002233 **
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Plotting Model Coefficients: 

°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ 

Starting new analysis, with data index DIDX "10" and formula index FIDX "4" in Summary Tables.
Using formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 05_results_euk_asv00_shal_UNIF_model_data_2020-Jan-31-14-16-03_with_hon_info.csv. 

Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.
- Setting types.
- Input dimensions are: 69 17 .
- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST VOY_FREQ B_FON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .
- Kept variables are: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST .
- Intermediate dimensions are: 69 6 .
- Undefined rows have been removed, assuming they were real "NA" and not "0".
- Final dimensions are: 48 6 .
# A tibble: 48 x 6
   RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT  DEST 
          <dbl>       <dbl>    <dbl> <fct>    <fct> <fct>
 1        0.702    1.14e- 4   1.06   TRUE     AW    WL   
 2        0.634    5.55e- 3   1.56   TRUE     BT    AW   
 3        0.713    6.70e- 4   1.50   TRUE     BT    GH   
 4        0.800    1.01e- 4   1.52   FALSE    BT    HT   
 5        0.782    1.02e- 5   2.14   TRUE     BT    LB   
 6        0.760    9.45e-11   3.16   FALSE    BT    MI   
 7        0.801    1.77e- 4   1.56   FALSE    BT    NO   
 8        0.728    2.80e- 3   1.57   TRUE     BT    RT   
 9        0.728    8.76e- 3   0.921  FALSE    BT    WL   
10        0.764    1.49e- 4   2.25   TRUE     BT    ZB   
11        0.836    1.48e- 5   1.29   FALSE    CB    PL   
12        0.683    4.06e- 5   0.547  FALSE    CB    RC   
13        0.673    2.96e- 4   1.07   TRUE     GH    WL   
14        0.741    3.92e-12   2.79   TRUE     HN    CB   
15        0.840    0.         2.81   TRUE     HN    HT   
16        0.779    2.51e- 8   2.11   TRUE     HT    AW   
17        0.765    8.88e- 8   2.09   TRUE     HT    GH   
18        0.889    1.47e- 6   2.53   TRUE     HT    LB   
19        0.849    8.29e- 7   2.94   FALSE    HT    MI   
20        0.632    1.00e+ 0   0.0459 FALSE    HT    NO   
21        0.838    0.         2.74   TRUE     HT    PM   
22        0.701    1.60e- 8   2.23   TRUE     HT    RT   
23        0.653    3.73e- 6   1.55   FALSE    HT    WL   
24        0.874    0.         3.39   TRUE     HT    ZB   
25        0.735    1.26e- 7   1.94   FALSE    LB    CB   
26        0.721    8.69e- 6   1.50   TRUE     LB    MI   
27        0.745    0.         4.15   TRUE     MI    AW   
28        0.869    1.06e- 6   2.94   FALSE    MI    NO   
29        0.719    0.         3.38   TRUE     MI    OK   
30        0.785    0.         4.18   TRUE     MI    RT   
31        0.790    0.         3.49   TRUE     MI    ZB   
32        0.603    1.44e- 3   1.12   TRUE     RT    WL   
33        0.796    4.13e- 6   2.06   TRUE     SI    AD   
34        0.741    0.         4.01   TRUE     SI    AW   
35        0.743    6.55e-11   3.18   TRUE     SI    BT   
36        0.737    0.         3.18   TRUE     SI    CB   
37        0.797    0.         3.93   TRUE     SI    GH   
38        0.761    8.94e- 3   0.576  TRUE     SI    HN   
39        0.842    1.00e- 5   2.81   TRUE     SI    HT   
40        0.727    9.72e- 5   1.55   TRUE     SI    LB   
41        0.858    3.21e- 5   2.80   TRUE     SI    NO   
42        0.706    1.75e-12   3.17   TRUE     SI    OK   
43        0.841    0.         3.86   TRUE     SI    PL   
44        0.781    1.05e-10   2.54   TRUE     SI    PM   
45        0.699    1.44e-10   2.94   TRUE     SI    RC   
46        0.777    0.         4.05   TRUE     SI    RT   
47        0.777    0.         3.51   TRUE     SI    ZB   
48        0.813    4.24e- 7   2.77   TRUE     WL    ZB   

Modelling function received variables: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .

Modelling function received variables: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .

Getting Model Summary: 
Linear mixed model fit by maximum likelihood  ['lmerMod']
Formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) +      (1 | DEST)
   Data: data_item

     AIC      BIC   logLik deviance df.resid 
  -140.5   -127.4     77.2   -154.5       41 

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-1.9523 -0.3791 -0.1032  0.5563  1.9589 

Random effects:
 Groups   Name        Variance  Std.Dev.
 DEST     (Intercept) 0.0019538 0.04420 
 PORT     (Intercept) 0.0002001 0.01415 
 Residual             0.0012038 0.03470 
Number of obs: 48, groups:  DEST, 17; PORT, 11

Fixed effects:
              Estimate Std. Error t value
(Intercept)   0.721702   0.023949  30.135
B_HON_NOECO  -0.166182   0.044067  -3.771
PRED_ENV      0.027017   0.007465   3.619
ECO_DIFFTRUE -0.023079   0.016662  -1.385

Correlation of Fixed Effects:
            (Intr) B_HON_ PRED_E
B_HON_NOECO -0.329              
PRED_ENV    -0.634  0.363       
ECO_DIFFTRU -0.439  0.079 -0.142

Getting Model Coefficients from Summary: 
                Estimate  Std. Error   t value
(Intercept)   0.72170214 0.023949065 30.134877
B_HON_NOECO  -0.16618164 0.044067231 -3.771093
PRED_ENV      0.02701726 0.007465388  3.619003
ECO_DIFFTRUE -0.02307903 0.016662276 -1.385107

Getting Model ANOVA: 
Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + 
full_model:     (1 | DEST)
           Df     AIC     BIC logLik deviance  Chisq Chi Df Pr(>Chisq)   
null_model  6 -133.12 -121.90 72.563  -145.12                            
full_model  7 -140.47 -127.37 77.236  -154.47 9.3471      1   0.002233 **
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Plotting Model Coefficients: 

°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ 

Starting new analysis, with data index DIDX "11" and formula index FIDX "4" in Summary Tables.
Using formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 06_results_euk_otu99_shal_UNIF_model_data_2020-Jan-31-14-16-14_no_ph_with_hon_info.csv. 

Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.
- Setting types.
- Input dimensions are: 64 17 .
- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST VOY_FREQ B_FON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .
- Kept variables are: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST .
- Intermediate dimensions are: 64 6 .
- Undefined rows have been removed, assuming they were real "NA" and not "0".
- Final dimensions are: 48 6 .
# A tibble: 48 x 6
   RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT  DEST 
          <dbl>       <dbl>    <dbl> <fct>    <fct> <fct>
 1        0.716    1.14e- 4   1.06   TRUE     AW    WL   
 2        0.625    5.55e- 3   1.56   TRUE     BT    AW   
 3        0.705    6.70e- 4   1.50   TRUE     BT    GH   
 4        0.800    1.01e- 4   1.52   FALSE    BT    HT   
 5        0.777    1.02e- 5   2.14   TRUE     BT    LB   
 6        0.750    9.45e-11   3.16   FALSE    BT    MI   
 7        0.795    1.77e- 4   1.56   FALSE    BT    NO   
 8        0.735    2.80e- 3   1.57   TRUE     BT    RT   
 9        0.733    8.76e- 3   0.921  FALSE    BT    WL   
10        0.776    1.49e- 4   2.25   TRUE     BT    ZB   
11        0.840    1.48e- 5   1.29   FALSE    CB    PL   
12        0.675    4.06e- 5   0.547  FALSE    CB    RC   
13        0.675    2.96e- 4   1.07   TRUE     GH    WL   
14        0.731    3.92e-12   2.79   TRUE     HN    CB   
15        0.836    0.         2.81   TRUE     HN    HT   
16        0.781    2.51e- 8   2.11   TRUE     HT    AW   
17        0.757    8.88e- 8   2.09   TRUE     HT    GH   
18        0.891    1.47e- 6   2.53   TRUE     HT    LB   
19        0.852    8.29e- 7   2.94   FALSE    HT    MI   
20        0.619    1.00e+ 0   0.0459 FALSE    HT    NO   
21        0.830    0.         2.74   TRUE     HT    PM   
22        0.694    1.60e- 8   2.23   TRUE     HT    RT   
23        0.642    3.73e- 6   1.55   FALSE    HT    WL   
24        0.878    0.         3.39   TRUE     HT    ZB   
25        0.724    1.26e- 7   1.94   FALSE    LB    CB   
26        0.710    8.69e- 6   1.50   TRUE     LB    MI   
27        0.745    0.         4.15   TRUE     MI    AW   
28        0.870    1.06e- 6   2.94   FALSE    MI    NO   
29        0.706    0.         3.38   TRUE     MI    OK   
30        0.793    0.         4.18   TRUE     MI    RT   
31        0.801    0.         3.49   TRUE     MI    ZB   
32        0.607    1.44e- 3   1.12   TRUE     RT    WL   
33        0.805    4.13e- 6   2.06   TRUE     SI    AD   
34        0.734    0.         4.01   TRUE     SI    AW   
35        0.741    6.55e-11   3.18   TRUE     SI    BT   
36        0.736    0.         3.18   TRUE     SI    CB   
37        0.800    0.         3.93   TRUE     SI    GH   
38        0.765    8.94e- 3   0.576  TRUE     SI    HN   
39        0.843    1.00e- 5   2.81   TRUE     SI    HT   
40        0.721    9.72e- 5   1.55   TRUE     SI    LB   
41        0.857    3.21e- 5   2.80   TRUE     SI    NO   
42        0.706    1.75e-12   3.17   TRUE     SI    OK   
43        0.843    0.         3.86   TRUE     SI    PL   
44        0.784    1.05e-10   2.54   TRUE     SI    PM   
45        0.692    1.44e-10   2.94   TRUE     SI    RC   
46        0.777    0.         4.05   TRUE     SI    RT   
47        0.777    0.         3.51   TRUE     SI    ZB   
48        0.830    4.24e- 7   2.77   TRUE     WL    ZB   

Modelling function received variables: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .

Modelling function received variables: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Getting Model Summary: 
Linear mixed model fit by maximum likelihood  ['lmerMod']
Formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) +      (1 | DEST)
   Data: data_item

     AIC      BIC   logLik deviance df.resid 
  -132.8   -119.7     73.4   -146.8       41 

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-1.91692 -0.43117 -0.08008  0.63312  2.04433 

Random effects:
 Groups   Name        Variance  Std.Dev.
 DEST     (Intercept) 0.0019633 0.04431 
 PORT     (Intercept) 0.0001145 0.01070 
 Residual             0.0015799 0.03975 
Number of obs: 48, groups:  DEST, 17; PORT, 11

Fixed effects:
              Estimate Std. Error t value
(Intercept)   0.719919   0.024941  28.865
B_HON_NOECO  -0.164333   0.049329  -3.331
PRED_ENV      0.026864   0.007967   3.372
ECO_DIFFTRUE -0.022834   0.018292  -1.248

Correlation of Fixed Effects:
            (Intr) B_HON_ PRED_E
B_HON_NOECO -0.330              
PRED_ENV    -0.636  0.346       
ECO_DIFFTRU -0.422  0.072 -0.196

Getting Model Coefficients from Summary: 
                Estimate  Std. Error   t value
(Intercept)   0.71991929 0.024940606 28.865348
B_HON_NOECO  -0.16433283 0.049329310 -3.331343
PRED_ENV      0.02686350 0.007967464  3.371650
ECO_DIFFTRUE -0.02283432 0.018291801 -1.248336

Getting Model ANOVA: 
Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + 
full_model:     (1 | DEST)
           Df     AIC    BIC logLik deviance  Chisq Chi Df Pr(>Chisq)   
null_model  6 -126.93 -115.7 69.464  -138.93                            
full_model  7 -132.80 -119.7 73.402  -146.80 7.8757      1    0.00501 **
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Plotting Model Coefficients: 

°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ 

Starting new analysis, with data index DIDX "12" and formula index FIDX "4" in Summary Tables.
Using formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 06_results_euk_otu99_shal_UNIF_model_data_2020-Jan-31-14-16-14_with_hon_info.csv. 

Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.
- Setting types.
- Input dimensions are: 69 17 .
- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST VOY_FREQ B_FON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .
- Kept variables are: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST .
- Intermediate dimensions are: 69 6 .
- Undefined rows have been removed, assuming they were real "NA" and not "0".
- Final dimensions are: 48 6 .
# A tibble: 48 x 6
   RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT  DEST 
          <dbl>       <dbl>    <dbl> <fct>    <fct> <fct>
 1        0.716    1.14e- 4   1.06   TRUE     AW    WL   
 2        0.625    5.55e- 3   1.56   TRUE     BT    AW   
 3        0.705    6.70e- 4   1.50   TRUE     BT    GH   
 4        0.800    1.01e- 4   1.52   FALSE    BT    HT   
 5        0.777    1.02e- 5   2.14   TRUE     BT    LB   
 6        0.750    9.45e-11   3.16   FALSE    BT    MI   
 7        0.795    1.77e- 4   1.56   FALSE    BT    NO   
 8        0.735    2.80e- 3   1.57   TRUE     BT    RT   
 9        0.733    8.76e- 3   0.921  FALSE    BT    WL   
10        0.776    1.49e- 4   2.25   TRUE     BT    ZB   
11        0.840    1.48e- 5   1.29   FALSE    CB    PL   
12        0.675    4.06e- 5   0.547  FALSE    CB    RC   
13        0.675    2.96e- 4   1.07   TRUE     GH    WL   
14        0.731    3.92e-12   2.79   TRUE     HN    CB   
15        0.836    0.         2.81   TRUE     HN    HT   
16        0.781    2.51e- 8   2.11   TRUE     HT    AW   
17        0.757    8.88e- 8   2.09   TRUE     HT    GH   
18        0.891    1.47e- 6   2.53   TRUE     HT    LB   
19        0.852    8.29e- 7   2.94   FALSE    HT    MI   
20        0.619    1.00e+ 0   0.0459 FALSE    HT    NO   
21        0.830    0.         2.74   TRUE     HT    PM   
22        0.694    1.60e- 8   2.23   TRUE     HT    RT   
23        0.642    3.73e- 6   1.55   FALSE    HT    WL   
24        0.878    0.         3.39   TRUE     HT    ZB   
25        0.724    1.26e- 7   1.94   FALSE    LB    CB   
26        0.710    8.69e- 6   1.50   TRUE     LB    MI   
27        0.745    0.         4.15   TRUE     MI    AW   
28        0.870    1.06e- 6   2.94   FALSE    MI    NO   
29        0.706    0.         3.38   TRUE     MI    OK   
30        0.793    0.         4.18   TRUE     MI    RT   
31        0.801    0.         3.49   TRUE     MI    ZB   
32        0.607    1.44e- 3   1.12   TRUE     RT    WL   
33        0.805    4.13e- 6   2.06   TRUE     SI    AD   
34        0.734    0.         4.01   TRUE     SI    AW   
35        0.741    6.55e-11   3.18   TRUE     SI    BT   
36        0.736    0.         3.18   TRUE     SI    CB   
37        0.800    0.         3.93   TRUE     SI    GH   
38        0.765    8.94e- 3   0.576  TRUE     SI    HN   
39        0.843    1.00e- 5   2.81   TRUE     SI    HT   
40        0.721    9.72e- 5   1.55   TRUE     SI    LB   
41        0.857    3.21e- 5   2.80   TRUE     SI    NO   
42        0.706    1.75e-12   3.17   TRUE     SI    OK   
43        0.843    0.         3.86   TRUE     SI    PL   
44        0.784    1.05e-10   2.54   TRUE     SI    PM   
45        0.692    1.44e-10   2.94   TRUE     SI    RC   
46        0.777    0.         4.05   TRUE     SI    RT   
47        0.777    0.         3.51   TRUE     SI    ZB   
48        0.830    4.24e- 7   2.77   TRUE     WL    ZB   

Modelling function received variables: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .

Modelling function received variables: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Getting Model Summary: 
Linear mixed model fit by maximum likelihood  ['lmerMod']
Formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) +      (1 | DEST)
   Data: data_item

     AIC      BIC   logLik deviance df.resid 
  -132.8   -119.7     73.4   -146.8       41 

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-1.91692 -0.43117 -0.08008  0.63312  2.04433 

Random effects:
 Groups   Name        Variance  Std.Dev.
 DEST     (Intercept) 0.0019633 0.04431 
 PORT     (Intercept) 0.0001145 0.01070 
 Residual             0.0015799 0.03975 
Number of obs: 48, groups:  DEST, 17; PORT, 11

Fixed effects:
              Estimate Std. Error t value
(Intercept)   0.719919   0.024941  28.865
B_HON_NOECO  -0.164333   0.049329  -3.331
PRED_ENV      0.026864   0.007967   3.372
ECO_DIFFTRUE -0.022834   0.018292  -1.248

Correlation of Fixed Effects:
            (Intr) B_HON_ PRED_E
B_HON_NOECO -0.330              
PRED_ENV    -0.636  0.346       
ECO_DIFFTRU -0.422  0.072 -0.196

Getting Model Coefficients from Summary: 
                Estimate  Std. Error   t value
(Intercept)   0.71991929 0.024940606 28.865348
B_HON_NOECO  -0.16433283 0.049329310 -3.331343
PRED_ENV      0.02686350 0.007967464  3.371650
ECO_DIFFTRUE -0.02283432 0.018291801 -1.248336

Getting Model ANOVA: 
Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + 
full_model:     (1 | DEST)
           Df     AIC    BIC logLik deviance  Chisq Chi Df Pr(>Chisq)   
null_model  6 -126.93 -115.7 69.464  -138.93                            
full_model  7 -132.80 -119.7 73.402  -146.80 7.8757      1    0.00501 **
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Plotting Model Coefficients: 

°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ 

Starting new analysis, with data index DIDX "13" and formula index FIDX "4" in Summary Tables.
Using formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 07_results_euk_asv00_shal_JAQU_model_data_2020-Jan-31-14-16-25_no_ph_with_hon_info.csv. 

Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.
- Setting types.
- Input dimensions are: 64 17 .
- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST VOY_FREQ B_FON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .
- Kept variables are: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST .
- Intermediate dimensions are: 64 6 .
- Undefined rows have been removed, assuming they were real "NA" and not "0".
- Final dimensions are: 48 6 .
# A tibble: 48 x 6
   RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT  DEST 
          <dbl>       <dbl>    <dbl> <fct>    <fct> <fct>
 1        0.951    1.14e- 4   1.06   TRUE     AW    WL   
 2        0.902    5.55e- 3   1.56   TRUE     BT    AW   
 3        0.943    6.70e- 4   1.50   TRUE     BT    GH   
 4        0.989    1.01e- 4   1.52   FALSE    BT    HT   
 5        0.964    1.02e- 5   2.14   TRUE     BT    LB   
 6        0.974    9.45e-11   3.16   FALSE    BT    MI   
 7        0.985    1.77e- 4   1.56   FALSE    BT    NO   
 8        0.955    2.80e- 3   1.57   TRUE     BT    RT   
 9        0.957    8.76e- 3   0.921  FALSE    BT    WL   
10        0.956    1.49e- 4   2.25   TRUE     BT    ZB   
11        1        1.48e- 5   1.29   FALSE    CB    PL   
12        0.907    4.06e- 5   0.547  FALSE    CB    RC   
13        0.913    2.96e- 4   1.07   TRUE     GH    WL   
14        0.980    3.92e-12   2.79   TRUE     HN    CB   
15        0.993    0.         2.81   TRUE     HN    HT   
16        0.988    2.51e- 8   2.11   TRUE     HT    AW   
17        0.969    8.88e- 8   2.09   TRUE     HT    GH   
18        1.00     1.47e- 6   2.53   TRUE     HT    LB   
19        0.994    8.29e- 7   2.94   FALSE    HT    MI   
20        0.891    1.00e+ 0   0.0459 FALSE    HT    NO   
21        0.997    0.         2.74   TRUE     HT    PM   
22        0.948    1.60e- 8   2.23   TRUE     HT    RT   
23        0.905    3.73e- 6   1.55   FALSE    HT    WL   
24        0.998    0.         3.39   TRUE     HT    ZB   
25        0.939    1.26e- 7   1.94   FALSE    LB    CB   
26        0.942    8.69e- 6   1.50   TRUE     LB    MI   
27        0.988    0.         4.15   TRUE     MI    AW   
28        0.998    1.06e- 6   2.94   FALSE    MI    NO   
29        0.962    0.         3.38   TRUE     MI    OK   
30        0.991    0.         4.18   TRUE     MI    RT   
31        0.988    0.         3.49   TRUE     MI    ZB   
32        0.894    1.44e- 3   1.12   TRUE     RT    WL   
33        0.971    4.13e- 6   2.06   TRUE     SI    AD   
34        0.986    0.         4.01   TRUE     SI    AW   
35        0.973    6.55e-11   3.18   TRUE     SI    BT   
36        0.982    0.         3.18   TRUE     SI    CB   
37        0.996    0.         3.93   TRUE     SI    GH   
38        0.958    8.94e- 3   0.576  TRUE     SI    HN   
39        0.997    1.00e- 5   2.81   TRUE     SI    HT   
40        0.966    9.72e- 5   1.55   TRUE     SI    LB   
41        0.998    3.21e- 5   2.80   TRUE     SI    NO   
42        0.959    1.75e-12   3.17   TRUE     SI    OK   
43        0.998    0.         3.86   TRUE     SI    PL   
44        0.996    1.05e-10   2.54   TRUE     SI    PM   
45        0.967    1.44e-10   2.94   TRUE     SI    RC   
46        0.993    0.         4.05   TRUE     SI    RT   
47        0.986    0.         3.51   TRUE     SI    ZB   
48        0.996    4.24e- 7   2.77   TRUE     WL    ZB   

Modelling function received variables: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Modelling function received variables: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Getting Model Summary: 
Linear mixed model fit by maximum likelihood  ['lmerMod']
Formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) +      (1 | DEST)
   Data: data_item

     AIC      BIC   logLik deviance df.resid 
  -221.0   -207.9    117.5   -235.0       41 

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-2.39075 -0.47440  0.00868  0.58801  1.80609 

Random effects:
 Groups   Name        Variance  Std.Dev.
 DEST     (Intercept) 0.0001525 0.01235 
 PORT     (Intercept) 0.0000000 0.00000 
 Residual             0.0003291 0.01814 
Number of obs: 48, groups:  DEST, 17; PORT, 11

Fixed effects:
              Estimate Std. Error t value
(Intercept)   0.933231   0.009376  99.539
B_HON_NOECO  -0.054326   0.021335  -2.546
PRED_ENV      0.017866   0.003212   5.562
ECO_DIFFTRUE -0.007158   0.007628  -0.938

Correlation of Fixed Effects:
            (Intr) B_HON_ PRED_E
B_HON_NOECO -0.352              
PRED_ENV    -0.655  0.312       
ECO_DIFFTRU -0.407  0.087 -0.280
convergence code: 0
boundary (singular) fit: see ?isSingular

Getting Model Coefficients from Summary: 
                Estimate  Std. Error    t value
(Intercept)   0.93323112 0.009375543 99.5388866
B_HON_NOECO  -0.05432616 0.021334568 -2.5463913
PRED_ENV      0.01786583 0.003211917  5.5623586
ECO_DIFFTRUE -0.00715808 0.007627890 -0.9384089

Getting Model ANOVA: 
Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + 
full_model:     (1 | DEST)
           Df     AIC     BIC logLik deviance  Chisq Chi Df Pr(>Chisq)  
null_model  6 -217.57 -206.34 114.79  -229.57                           
full_model  7 -221.04 -207.94 117.52  -235.04 5.4671      1    0.01938 *
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Plotting Model Coefficients: 
boundary (singular) fit: see ?isSingular

°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ 

Starting new analysis, with data index DIDX "14" and formula index FIDX "4" in Summary Tables.
Using formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 07_results_euk_asv00_shal_JAQU_model_data_2020-Jan-31-14-16-25_with_hon_info.csv. 

Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.
- Setting types.
- Input dimensions are: 69 17 .
- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST VOY_FREQ B_FON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .
- Kept variables are: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST .
- Intermediate dimensions are: 69 6 .
- Undefined rows have been removed, assuming they were real "NA" and not "0".
- Final dimensions are: 48 6 .
# A tibble: 48 x 6
   RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT  DEST 
          <dbl>       <dbl>    <dbl> <fct>    <fct> <fct>
 1        0.951    1.14e- 4   1.06   TRUE     AW    WL   
 2        0.902    5.55e- 3   1.56   TRUE     BT    AW   
 3        0.943    6.70e- 4   1.50   TRUE     BT    GH   
 4        0.989    1.01e- 4   1.52   FALSE    BT    HT   
 5        0.964    1.02e- 5   2.14   TRUE     BT    LB   
 6        0.974    9.45e-11   3.16   FALSE    BT    MI   
 7        0.985    1.77e- 4   1.56   FALSE    BT    NO   
 8        0.955    2.80e- 3   1.57   TRUE     BT    RT   
 9        0.957    8.76e- 3   0.921  FALSE    BT    WL   
10        0.956    1.49e- 4   2.25   TRUE     BT    ZB   
11        1        1.48e- 5   1.29   FALSE    CB    PL   
12        0.907    4.06e- 5   0.547  FALSE    CB    RC   
13        0.913    2.96e- 4   1.07   TRUE     GH    WL   
14        0.980    3.92e-12   2.79   TRUE     HN    CB   
15        0.993    0.         2.81   TRUE     HN    HT   
16        0.988    2.51e- 8   2.11   TRUE     HT    AW   
17        0.969    8.88e- 8   2.09   TRUE     HT    GH   
18        1.00     1.47e- 6   2.53   TRUE     HT    LB   
19        0.994    8.29e- 7   2.94   FALSE    HT    MI   
20        0.891    1.00e+ 0   0.0459 FALSE    HT    NO   
21        0.997    0.         2.74   TRUE     HT    PM   
22        0.948    1.60e- 8   2.23   TRUE     HT    RT   
23        0.905    3.73e- 6   1.55   FALSE    HT    WL   
24        0.998    0.         3.39   TRUE     HT    ZB   
25        0.939    1.26e- 7   1.94   FALSE    LB    CB   
26        0.942    8.69e- 6   1.50   TRUE     LB    MI   
27        0.988    0.         4.15   TRUE     MI    AW   
28        0.998    1.06e- 6   2.94   FALSE    MI    NO   
29        0.962    0.         3.38   TRUE     MI    OK   
30        0.991    0.         4.18   TRUE     MI    RT   
31        0.988    0.         3.49   TRUE     MI    ZB   
32        0.894    1.44e- 3   1.12   TRUE     RT    WL   
33        0.971    4.13e- 6   2.06   TRUE     SI    AD   
34        0.986    0.         4.01   TRUE     SI    AW   
35        0.973    6.55e-11   3.18   TRUE     SI    BT   
36        0.982    0.         3.18   TRUE     SI    CB   
37        0.996    0.         3.93   TRUE     SI    GH   
38        0.958    8.94e- 3   0.576  TRUE     SI    HN   
39        0.997    1.00e- 5   2.81   TRUE     SI    HT   
40        0.966    9.72e- 5   1.55   TRUE     SI    LB   
41        0.998    3.21e- 5   2.80   TRUE     SI    NO   
42        0.959    1.75e-12   3.17   TRUE     SI    OK   
43        0.998    0.         3.86   TRUE     SI    PL   
44        0.996    1.05e-10   2.54   TRUE     SI    PM   
45        0.967    1.44e-10   2.94   TRUE     SI    RC   
46        0.993    0.         4.05   TRUE     SI    RT   
47        0.986    0.         3.51   TRUE     SI    ZB   
48        0.996    4.24e- 7   2.77   TRUE     WL    ZB   

Modelling function received variables: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Modelling function received variables: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Getting Model Summary: 
Linear mixed model fit by maximum likelihood  ['lmerMod']
Formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) +      (1 | DEST)
   Data: data_item

     AIC      BIC   logLik deviance df.resid 
  -221.0   -207.9    117.5   -235.0       41 

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-2.39075 -0.47440  0.00868  0.58801  1.80609 

Random effects:
 Groups   Name        Variance  Std.Dev.
 DEST     (Intercept) 0.0001525 0.01235 
 PORT     (Intercept) 0.0000000 0.00000 
 Residual             0.0003291 0.01814 
Number of obs: 48, groups:  DEST, 17; PORT, 11

Fixed effects:
              Estimate Std. Error t value
(Intercept)   0.933231   0.009376  99.539
B_HON_NOECO  -0.054326   0.021335  -2.546
PRED_ENV      0.017866   0.003212   5.562
ECO_DIFFTRUE -0.007158   0.007628  -0.938

Correlation of Fixed Effects:
            (Intr) B_HON_ PRED_E
B_HON_NOECO -0.352              
PRED_ENV    -0.655  0.312       
ECO_DIFFTRU -0.407  0.087 -0.280
convergence code: 0
boundary (singular) fit: see ?isSingular

Getting Model Coefficients from Summary: 
                Estimate  Std. Error    t value
(Intercept)   0.93323112 0.009375543 99.5388866
B_HON_NOECO  -0.05432616 0.021334568 -2.5463913
PRED_ENV      0.01786583 0.003211917  5.5623586
ECO_DIFFTRUE -0.00715808 0.007627890 -0.9384089

Getting Model ANOVA: 
Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + 
full_model:     (1 | DEST)
           Df     AIC     BIC logLik deviance  Chisq Chi Df Pr(>Chisq)  
null_model  6 -217.57 -206.34 114.79  -229.57                           
full_model  7 -221.04 -207.94 117.52  -235.04 5.4671      1    0.01938 *
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Plotting Model Coefficients: 
boundary (singular) fit: see ?isSingular

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Starting new analysis, with data index DIDX "15" and formula index FIDX "4" in Summary Tables.
Using formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 08_results_euk_otu99_shal_JAQU_model_data_2020-Jan-31-14-16-36_no_ph_with_hon_info.csv. 

Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.
- Setting types.
- Input dimensions are: 64 17 .
- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST VOY_FREQ B_FON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .
- Kept variables are: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST .
- Intermediate dimensions are: 64 6 .
- Undefined rows have been removed, assuming they were real "NA" and not "0".
- Final dimensions are: 48 6 .
# A tibble: 48 x 6
   RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT  DEST 
          <dbl>       <dbl>    <dbl> <fct>    <fct> <fct>
 1        0.919    1.14e- 4   1.06   TRUE     AW    WL   
 2        0.847    5.55e- 3   1.56   TRUE     BT    AW   
 3        0.906    6.70e- 4   1.50   TRUE     BT    GH   
 4        0.980    1.01e- 4   1.52   FALSE    BT    HT   
 5        0.936    1.02e- 5   2.14   TRUE     BT    LB   
 6        0.933    9.45e-11   3.16   FALSE    BT    MI   
 7        0.971    1.77e- 4   1.56   FALSE    BT    NO   
 8        0.927    2.80e- 3   1.57   TRUE     BT    RT   
 9        0.934    8.76e- 3   0.921  FALSE    BT    WL   
10        0.931    1.49e- 4   2.25   TRUE     BT    ZB   
11        0.998    1.48e- 5   1.29   FALSE    CB    PL   
12        0.863    4.06e- 5   0.547  FALSE    CB    RC   
13        0.879    2.96e- 4   1.07   TRUE     GH    WL   
14        0.947    3.92e-12   2.79   TRUE     HN    CB   
15        0.988    0.         2.81   TRUE     HN    HT   
16        0.981    2.51e- 8   2.11   TRUE     HT    AW   
17        0.951    8.88e- 8   2.09   TRUE     HT    GH   
18        0.998    1.47e- 6   2.53   TRUE     HT    LB   
19        0.987    8.29e- 7   2.94   FALSE    HT    MI   
20        0.848    1.00e+ 0   0.0459 FALSE    HT    NO   
21        0.995    0.         2.74   TRUE     HT    PM   
22        0.920    1.60e- 8   2.23   TRUE     HT    RT   
23        0.865    3.73e- 6   1.55   FALSE    HT    WL   
24        0.997    0.         3.39   TRUE     HT    ZB   
25        0.901    1.26e- 7   1.94   FALSE    LB    CB   
26        0.907    8.69e- 6   1.50   TRUE     LB    MI   
27        0.966    0.         4.15   TRUE     MI    AW   
28        0.990    1.06e- 6   2.94   FALSE    MI    NO   
29        0.927    0.         3.38   TRUE     MI    OK   
30        0.976    0.         4.18   TRUE     MI    RT   
31        0.965    0.         3.49   TRUE     MI    ZB   
32        0.839    1.44e- 3   1.12   TRUE     RT    WL   
33        0.945    4.13e- 6   2.06   TRUE     SI    AD   
34        0.960    0.         4.01   TRUE     SI    AW   
35        0.947    6.55e-11   3.18   TRUE     SI    BT   
36        0.950    0.         3.18   TRUE     SI    CB   
37        0.986    0.         3.93   TRUE     SI    GH   
38        0.935    8.94e- 3   0.576  TRUE     SI    HN   
39        0.994    1.00e- 5   2.81   TRUE     SI    HT   
40        0.931    9.72e- 5   1.55   TRUE     SI    LB   
41        0.995    3.21e- 5   2.80   TRUE     SI    NO   
42        0.919    1.75e-12   3.17   TRUE     SI    OK   
43        0.997    0.         3.86   TRUE     SI    PL   
44        0.983    1.05e-10   2.54   TRUE     SI    PM   
45        0.928    1.44e-10   2.94   TRUE     SI    RC   
46        0.976    0.         4.05   TRUE     SI    RT   
47        0.952    0.         3.51   TRUE     SI    ZB   
48        0.987    4.24e- 7   2.77   TRUE     WL    ZB   

Modelling function received variables: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Modelling function received variables: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Getting Model Summary: 
Linear mixed model fit by maximum likelihood  ['lmerMod']
Formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) +      (1 | DEST)
   Data: data_item

     AIC      BIC   logLik deviance df.resid 
  -179.1   -166.0     96.5   -193.1       41 

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-2.37300 -0.53155 -0.01222  0.56566  2.04362 

Random effects:
 Groups   Name        Variance  Std.Dev.
 DEST     (Intercept) 0.0004388 0.02095 
 PORT     (Intercept) 0.0000000 0.00000 
 Residual             0.0007533 0.02745 
Number of obs: 48, groups:  DEST, 17; PORT, 11

Fixed effects:
              Estimate Std. Error t value
(Intercept)   0.901945   0.014542  62.025
B_HON_NOECO  -0.075597   0.032511  -2.325
PRED_ENV      0.022821   0.004927   4.632
ECO_DIFFTRUE -0.011451   0.011687  -0.980

Correlation of Fixed Effects:
            (Intr) B_HON_ PRED_E
B_HON_NOECO -0.346              
PRED_ENV    -0.649  0.316       
ECO_DIFFTRU -0.405  0.079 -0.278
convergence code: 0
boundary (singular) fit: see ?isSingular

Getting Model Coefficients from Summary: 
                Estimate  Std. Error   t value
(Intercept)   0.90194454 0.014541696 62.024712
B_HON_NOECO  -0.07559749 0.032511184 -2.325276
PRED_ENV      0.02282126 0.004926948  4.631925
ECO_DIFFTRUE -0.01145129 0.011687114 -0.979822

Getting Model ANOVA: 
Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + 
full_model:     (1 | DEST)
           Df     AIC     BIC logLik deviance  Chisq Chi Df Pr(>Chisq)  
null_model  6 -176.43 -165.20 94.215  -188.43                           
full_model  7 -179.09 -165.99 96.546  -193.09 4.6622      1    0.03083 *
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Plotting Model Coefficients: 
boundary (singular) fit: see ?isSingular

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Starting new analysis, with data index DIDX "16" and formula index FIDX "4" in Summary Tables.
Using formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) with data: 08_results_euk_otu99_shal_JAQU_model_data_2020-Jan-31-14-16-36_with_hon_info.csv. 

Data is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.
- Setting types.
- Input dimensions are: 69 17 .
- Removed variables are: PRED_TRIPS ECO_PORT ECO_DEST VOY_FREQ B_FON_NOECO B_FON_SMECO B_HON_SMECO F_FON_NOECO F_HON_NOECO F_FON_SMECO F_HON_SMECO .
- Kept variables are: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST .
- Intermediate dimensions are: 69 6 .
- Undefined rows have been removed, assuming they were real "NA" and not "0".
- Final dimensions are: 48 6 .
# A tibble: 48 x 6
   RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT  DEST 
          <dbl>       <dbl>    <dbl> <fct>    <fct> <fct>
 1        0.919    1.14e- 4   1.06   TRUE     AW    WL   
 2        0.847    5.55e- 3   1.56   TRUE     BT    AW   
 3        0.906    6.70e- 4   1.50   TRUE     BT    GH   
 4        0.980    1.01e- 4   1.52   FALSE    BT    HT   
 5        0.936    1.02e- 5   2.14   TRUE     BT    LB   
 6        0.933    9.45e-11   3.16   FALSE    BT    MI   
 7        0.971    1.77e- 4   1.56   FALSE    BT    NO   
 8        0.927    2.80e- 3   1.57   TRUE     BT    RT   
 9        0.934    8.76e- 3   0.921  FALSE    BT    WL   
10        0.931    1.49e- 4   2.25   TRUE     BT    ZB   
11        0.998    1.48e- 5   1.29   FALSE    CB    PL   
12        0.863    4.06e- 5   0.547  FALSE    CB    RC   
13        0.879    2.96e- 4   1.07   TRUE     GH    WL   
14        0.947    3.92e-12   2.79   TRUE     HN    CB   
15        0.988    0.         2.81   TRUE     HN    HT   
16        0.981    2.51e- 8   2.11   TRUE     HT    AW   
17        0.951    8.88e- 8   2.09   TRUE     HT    GH   
18        0.998    1.47e- 6   2.53   TRUE     HT    LB   
19        0.987    8.29e- 7   2.94   FALSE    HT    MI   
20        0.848    1.00e+ 0   0.0459 FALSE    HT    NO   
21        0.995    0.         2.74   TRUE     HT    PM   
22        0.920    1.60e- 8   2.23   TRUE     HT    RT   
23        0.865    3.73e- 6   1.55   FALSE    HT    WL   
24        0.997    0.         3.39   TRUE     HT    ZB   
25        0.901    1.26e- 7   1.94   FALSE    LB    CB   
26        0.907    8.69e- 6   1.50   TRUE     LB    MI   
27        0.966    0.         4.15   TRUE     MI    AW   
28        0.990    1.06e- 6   2.94   FALSE    MI    NO   
29        0.927    0.         3.38   TRUE     MI    OK   
30        0.976    0.         4.18   TRUE     MI    RT   
31        0.965    0.         3.49   TRUE     MI    ZB   
32        0.839    1.44e- 3   1.12   TRUE     RT    WL   
33        0.945    4.13e- 6   2.06   TRUE     SI    AD   
34        0.960    0.         4.01   TRUE     SI    AW   
35        0.947    6.55e-11   3.18   TRUE     SI    BT   
36        0.950    0.         3.18   TRUE     SI    CB   
37        0.986    0.         3.93   TRUE     SI    GH   
38        0.935    8.94e- 3   0.576  TRUE     SI    HN   
39        0.994    1.00e- 5   2.81   TRUE     SI    HT   
40        0.931    9.72e- 5   1.55   TRUE     SI    LB   
41        0.995    3.21e- 5   2.80   TRUE     SI    NO   
42        0.919    1.75e-12   3.17   TRUE     SI    OK   
43        0.997    0.         3.86   TRUE     SI    PL   
44        0.983    1.05e-10   2.54   TRUE     SI    PM   
45        0.928    1.44e-10   2.94   TRUE     SI    RC   
46        0.976    0.         4.05   TRUE     SI    RT   
47        0.952    0.         3.51   TRUE     SI    ZB   
48        0.987    4.24e- 7   2.77   TRUE     WL    ZB   

Modelling function received variables: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Modelling function received variables: RESP_UNIFRAC B_HON_NOECO PRED_ENV ECO_DIFF PORT DEST .
   ... dimensions: 48 6 .
   ... formula: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST) .
boundary (singular) fit: see ?isSingular

Getting Model Summary: 
Linear mixed model fit by maximum likelihood  ['lmerMod']
Formula: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) +      (1 | DEST)
   Data: data_item

     AIC      BIC   logLik deviance df.resid 
  -179.1   -166.0     96.5   -193.1       41 

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-2.37300 -0.53155 -0.01222  0.56566  2.04362 

Random effects:
 Groups   Name        Variance  Std.Dev.
 DEST     (Intercept) 0.0004388 0.02095 
 PORT     (Intercept) 0.0000000 0.00000 
 Residual             0.0007533 0.02745 
Number of obs: 48, groups:  DEST, 17; PORT, 11

Fixed effects:
              Estimate Std. Error t value
(Intercept)   0.901945   0.014542  62.025
B_HON_NOECO  -0.075597   0.032511  -2.325
PRED_ENV      0.022821   0.004927   4.632
ECO_DIFFTRUE -0.011451   0.011687  -0.980

Correlation of Fixed Effects:
            (Intr) B_HON_ PRED_E
B_HON_NOECO -0.346              
PRED_ENV    -0.649  0.316       
ECO_DIFFTRU -0.405  0.079 -0.278
convergence code: 0
boundary (singular) fit: see ?isSingular

Getting Model Coefficients from Summary: 
                Estimate  Std. Error   t value
(Intercept)   0.90194454 0.014541696 62.024712
B_HON_NOECO  -0.07559749 0.032511184 -2.325276
PRED_ENV      0.02282126 0.004926948  4.631925
ECO_DIFFTRUE -0.01145129 0.011687114 -0.979822

Getting Model ANOVA: 
Data: data_item
Models:
null_model: RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)
full_model: RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + 
full_model:     (1 | DEST)
           Df     AIC     BIC logLik deviance  Chisq Chi Df Pr(>Chisq)  
null_model  6 -176.43 -165.20 94.215  -188.43                           
full_model  7 -179.09 -165.99 96.546  -193.09 4.6622      1    0.03083 *
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Plotting Model Coefficients: 
boundary (singular) fit: see ?isSingular

Show Results table

Check above raw model out put for Writing above results to results table row: n and look up n in both tables below.

Sort results table by AIC

analysis_summaries <- arrange(analysis_summaries, AKAI)

Show results table interactively:

analysis_summaries

Show results table on screen:

print(analysis_summaries, n = Inf)
# A tibble: 64 x 6
    DIDX  FIDX  AKAI    PVAL FRML                                                          DATA                                                                 
   <int> <int> <dbl>   <dbl> <chr>                                                         <chr>                                                                
 1     6     1 -305. 0.186   RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT)… 03_results_euk_asv00_deep_JAQU_model_data_2020-Jan-31-14-15-41_with_…
 2    14     1 -304. 0.190   RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT)… 07_results_euk_asv00_shal_JAQU_model_data_2020-Jan-31-14-16-25_with_…
 3     5     1 -279. 0.191   RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT)… 03_results_euk_asv00_deep_JAQU_model_data_2020-Jan-31-14-15-41_no_ph…
 4    13     1 -279. 0.197   RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT)… 07_results_euk_asv00_shal_JAQU_model_data_2020-Jan-31-14-16-25_no_ph…
 5     8     1 -245. 0.247   RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT)… 04_results_euk_otu99_deep_JAQU_model_data_2020-Jan-31-14-15-52_with_…
 6    16     1 -244. 0.252   RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT)… 08_results_euk_otu99_shal_JAQU_model_data_2020-Jan-31-14-16-36_with_…
 7     7     1 -226. 0.272   RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT)… 04_results_euk_otu99_deep_JAQU_model_data_2020-Jan-31-14-15-52_no_ph…
 8    15     1 -224. 0.281   RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT)… 08_results_euk_otu99_shal_JAQU_model_data_2020-Jan-31-14-16-36_no_ph…
 9     5     4 -221. 0.0187  RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT… 03_results_euk_asv00_deep_JAQU_model_data_2020-Jan-31-14-15-41_no_ph…
10     6     4 -221. 0.0187  RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT… 03_results_euk_asv00_deep_JAQU_model_data_2020-Jan-31-14-15-41_with_…
11    13     4 -221. 0.0194  RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT… 07_results_euk_asv00_shal_JAQU_model_data_2020-Jan-31-14-16-25_no_ph…
12    14     4 -221. 0.0194  RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT… 07_results_euk_asv00_shal_JAQU_model_data_2020-Jan-31-14-16-25_with_…
13     5     2 -220. 0.0398  RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) +… 03_results_euk_asv00_deep_JAQU_model_data_2020-Jan-31-14-15-41_no_ph…
14     6     2 -220. 0.0398  RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) +… 03_results_euk_asv00_deep_JAQU_model_data_2020-Jan-31-14-15-41_with_…
15    13     2 -220. 0.0412  RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) +… 07_results_euk_asv00_shal_JAQU_model_data_2020-Jan-31-14-16-25_no_ph…
16    14     2 -220. 0.0412  RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) +… 07_results_euk_asv00_shal_JAQU_model_data_2020-Jan-31-14-16-25_with_…
17     5     3 -216. 0.557   RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT… 03_results_euk_asv00_deep_JAQU_model_data_2020-Jan-31-14-15-41_no_ph…
18     6     3 -216. 0.557   RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT… 03_results_euk_asv00_deep_JAQU_model_data_2020-Jan-31-14-15-41_with_…
19    13     3 -216. 0.548   RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT… 07_results_euk_asv00_shal_JAQU_model_data_2020-Jan-31-14-16-25_no_ph…
20    14     3 -216. 0.548   RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT… 07_results_euk_asv00_shal_JAQU_model_data_2020-Jan-31-14-16-25_with_…
21     7     4 -180. 0.0298  RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT… 04_results_euk_otu99_deep_JAQU_model_data_2020-Jan-31-14-15-52_no_ph…
22     8     4 -180. 0.0298  RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT… 04_results_euk_otu99_deep_JAQU_model_data_2020-Jan-31-14-15-52_with_…
23    15     4 -179. 0.0308  RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT… 08_results_euk_otu99_shal_JAQU_model_data_2020-Jan-31-14-16-36_no_ph…
24    16     4 -179. 0.0308  RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT… 08_results_euk_otu99_shal_JAQU_model_data_2020-Jan-31-14-16-36_with_…
25     7     2 -179. 0.0592  RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) +… 04_results_euk_otu99_deep_JAQU_model_data_2020-Jan-31-14-15-52_no_ph…
26     8     2 -179. 0.0592  RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) +… 04_results_euk_otu99_deep_JAQU_model_data_2020-Jan-31-14-15-52_with_…
27    15     2 -178. 0.0610  RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) +… 08_results_euk_otu99_shal_JAQU_model_data_2020-Jan-31-14-16-36_no_ph…
28    16     2 -178. 0.0610  RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) +… 08_results_euk_otu99_shal_JAQU_model_data_2020-Jan-31-14-16-36_with_…
29    10     1 -178. 0.0355  RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT)… 05_results_euk_asv00_shal_UNIF_model_data_2020-Jan-31-14-16-03_with_…
30     7     3 -176. 0.520   RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT… 04_results_euk_otu99_deep_JAQU_model_data_2020-Jan-31-14-15-52_no_ph…
31     8     3 -176. 0.520   RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT… 04_results_euk_otu99_deep_JAQU_model_data_2020-Jan-31-14-15-52_with_…
32     2     1 -175. 0.0431  RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT)… 01_results_euk_asv00_deep_UNIF_model_data_2020-Jan-31-14-15-13_with_…
33    15     3 -175. 0.526   RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT… 08_results_euk_otu99_shal_JAQU_model_data_2020-Jan-31-14-16-36_no_ph…
34    16     3 -175. 0.526   RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT… 08_results_euk_otu99_shal_JAQU_model_data_2020-Jan-31-14-16-36_with_…
35     4     1 -175. 0.0474  RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT)… 02_results_euk_otu99_deep_UNIF_model_data_2020-Jan-31-14-15-28_with_…
36    12     1 -169. 0.0457  RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT)… 06_results_euk_otu99_shal_UNIF_model_data_2020-Jan-31-14-16-14_with_…
37     9     1 -161. 0.0512  RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT)… 05_results_euk_asv00_shal_UNIF_model_data_2020-Jan-31-14-16-03_no_ph…
38     1     1 -159. 0.0617  RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT)… 01_results_euk_asv00_deep_UNIF_model_data_2020-Jan-31-14-15-13_no_ph…
39     3     1 -159. 0.0676  RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT)… 02_results_euk_otu99_deep_UNIF_model_data_2020-Jan-31-14-15-28_no_ph…
40    11     1 -154. 0.0660  RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT)… 06_results_euk_otu99_shal_UNIF_model_data_2020-Jan-31-14-16-14_no_ph…
41     9     4 -140. 0.00223 RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT… 05_results_euk_asv00_shal_UNIF_model_data_2020-Jan-31-14-16-03_no_ph…
42    10     4 -140. 0.00223 RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT… 05_results_euk_asv00_shal_UNIF_model_data_2020-Jan-31-14-16-03_with_…
43     1     4 -140. 0.00266 RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT… 01_results_euk_asv00_deep_UNIF_model_data_2020-Jan-31-14-15-13_no_ph…
44     2     4 -140. 0.00266 RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT… 01_results_euk_asv00_deep_UNIF_model_data_2020-Jan-31-14-15-13_with_…
45     9     2 -140. 0.00372 RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) +… 05_results_euk_asv00_shal_UNIF_model_data_2020-Jan-31-14-16-03_no_ph…
46    10     2 -140. 0.00372 RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) +… 05_results_euk_asv00_shal_UNIF_model_data_2020-Jan-31-14-16-03_with_…
47     2     2 -139. 0.00434 RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) +… 01_results_euk_asv00_deep_UNIF_model_data_2020-Jan-31-14-15-13_with_…
48     1     2 -139. 0.00434 RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) +… 01_results_euk_asv00_deep_UNIF_model_data_2020-Jan-31-14-15-13_no_ph…
49     3     4 -138. 0.00426 RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT… 02_results_euk_otu99_deep_UNIF_model_data_2020-Jan-31-14-15-28_no_ph…
50     4     4 -138. 0.00426 RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT… 02_results_euk_otu99_deep_UNIF_model_data_2020-Jan-31-14-15-28_with_…
51     3     2 -137. 0.00769 RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) +… 02_results_euk_otu99_deep_UNIF_model_data_2020-Jan-31-14-15-28_no_ph…
52     4     2 -137. 0.00769 RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) +… 02_results_euk_otu99_deep_UNIF_model_data_2020-Jan-31-14-15-28_with_…
53    11     4 -133. 0.00501 RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT… 06_results_euk_otu99_shal_UNIF_model_data_2020-Jan-31-14-16-14_no_ph…
54    12     4 -133. 0.00501 RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT… 06_results_euk_otu99_shal_UNIF_model_data_2020-Jan-31-14-16-14_with_…
55     9     3 -132. 0.299   RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT… 05_results_euk_asv00_shal_UNIF_model_data_2020-Jan-31-14-16-03_no_ph…
56    10     3 -132. 0.299   RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT… 05_results_euk_asv00_shal_UNIF_model_data_2020-Jan-31-14-16-03_with_…
57     2     3 -132. 0.336   RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT… 01_results_euk_asv00_deep_UNIF_model_data_2020-Jan-31-14-15-13_with_…
58     1     3 -132. 0.336   RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT… 01_results_euk_asv00_deep_UNIF_model_data_2020-Jan-31-14-15-13_no_ph…
59    11     2 -132. 0.00847 RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) +… 06_results_euk_otu99_shal_UNIF_model_data_2020-Jan-31-14-16-14_no_ph…
60    12     2 -132. 0.00847 RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) +… 06_results_euk_otu99_shal_UNIF_model_data_2020-Jan-31-14-16-14_with_…
61     3     3 -130. 0.388   RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT… 02_results_euk_otu99_deep_UNIF_model_data_2020-Jan-31-14-15-28_no_ph…
62     4     3 -130. 0.388   RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT… 02_results_euk_otu99_deep_UNIF_model_data_2020-Jan-31-14-15-28_with_…
63    11     3 -126. 0.349   RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT… 06_results_euk_otu99_shal_UNIF_model_data_2020-Jan-31-14-16-14_no_ph…
64    12     3 -126. 0.349   RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT… 06_results_euk_otu99_shal_UNIF_model_data_2020-Jan-31-14-16-14_with_…

On warning ?isSingular

Complex mixed-effect models (i.e., those with a large number of variance-covariance parameters) frequently result in singular fits, i.e. estimated variance-covariance matrices with less than full rank. Less technically, this means that some “dimensions” of the variance-covariance matrix have been estimated as exactly zero. For scalar random effects such as intercept-only models, or 2-dimensional random effects such as intercept+slope models, singularity is relatively easy to detect because it leads to random-effect variance estimates of (nearly) zero, or estimates of correlations that are (almost) exactly -1 or 1. However, for more complex models (variance-covariance matrices of dimension >=3) singularity can be hard to detect; models can often be singular without any of their individual variances being close to zero or correlations being close to +/-1.

This function performs a simple test to determine whether any of the random effects covariance matrices of a fitted model are singular. The rePCA method provides more detail about the singularity pattern, showing the standard deviations of orthogonal variance components and the mapping from variance terms in the model to orthogonal components (i.e., eigenvector/rotation matrices).

While singular models are statistically well defined (it is theoretically sensible for the true maximum likelihood estimate to correspond to a singular fit), there are real concerns that (1) singular fits correspond to overfitted models that may have poor power; (2) chances of numerical problems and mis-convergence are higher for singular models (e.g. it may be computationally difficult to compute profile confidence intervals for such models); (3) standard inferential procedures such as Wald statistics and likelihood ratio tests may be inappropriate.

There is not yet consensus about how to deal with singularity, or more generally to choose which random-effects specification (from a range of choices of varying complexity) to use. Some proposals include:

avoid fitting overly complex models in the first place, i.e. design experiments/restrict models a priori such that the variance-covariance matrices can be estimated precisely enough to avoid singularity (Matuschek et al 2017)

use some form of model selection to choose a model that balances predictive accuracy and overfitting/type I error (Bates et al 2015, Matuschek et al 2017)

“keep it maximal”, i.e. fit the most complex model consistent with the experimental design, removing only terms required to allow a non-singular fit (Barr et al. 2013), or removing further terms based on p-values or AIC

use a partially Bayesian method that produces maximum a posteriori (MAP) estimates using regularizing priors to force the estimated random-effects variance-covariance matrices away from singularity (Chung et al 2013, blme package)

use a fully Bayesian method that both regularizes the model via informative priors and gives estimates and credible intervals for all parameters that average over the uncertainty in the random effects parameters (Gelman and Hill 2006, McElreath 2015; MCMCglmm, rstanarm and brms packages) # Session info

The code and output in this document were tested and generated in the following computing environment:

R version 3.6.1 (2019-07-05)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS High Sierra 10.13.6

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib

Random number generation:
 RNG:     Mersenne-Twister 
 Normal:  Inversion 
 Sample:  Rounding 
 
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] magrittr_1.5        formula.tools_1.7.1 cowplot_1.0.0       sjPlot_2.8.2        lme4_1.1-21         Matrix_1.2-18       reshape2_1.4.3     
 [8] gdata_2.18.0        forcats_0.4.0       stringr_1.4.0       dplyr_0.8.3         purrr_0.3.3         readr_1.3.1         tidyr_1.0.2        
[15] tibble_2.1.3        ggplot2_3.2.1       tidyverse_1.3.0    

loaded via a namespace (and not attached):
 [1] nlme_3.1-143         fs_1.3.1             lubridate_1.7.4      RColorBrewer_1.1-2   insight_0.8.0        httr_1.4.1           tools_3.6.1         
 [8] backports_1.1.5      utf8_1.1.4           R6_2.4.1             sjlabelled_1.1.3     DBI_1.1.0            lazyeval_0.2.2       colorspace_1.4-1    
[15] withr_2.1.2          tidyselect_1.0.0     emmeans_1.4.4        compiler_3.6.1       performance_0.4.3    cli_2.0.1            rvest_0.3.5         
[22] xml2_1.2.2           sandwich_2.5-1       bayestestR_0.5.1     scales_1.1.0         mvtnorm_1.0-12       digest_0.6.23        minqa_1.2.4         
[29] rmarkdown_2.1        base64enc_0.1-3      pkgconfig_2.0.3      htmltools_0.4.0      dbplyr_1.4.2         highr_0.8            rlang_0.4.4         
[36] readxl_1.3.1         rstudioapi_0.10      farver_2.0.3         generics_0.0.2       zoo_1.8-7            jsonlite_1.6         gtools_3.8.1        
[43] parameters_0.4.1     Rcpp_1.0.3           munsell_0.5.0        fansi_0.4.1          lifecycle_0.1.0      stringi_1.4.5        multcomp_1.4-12     
[50] yaml_2.2.0           snakecase_0.11.0     MASS_7.3-51.5        plyr_1.8.5           grid_3.6.1           sjmisc_2.8.3         crayon_1.3.4        
[57] lattice_0.20-38      ggeffects_0.14.1     haven_2.2.0          splines_3.6.1        sjstats_0.17.8       hms_0.5.3            knitr_1.27          
[64] pillar_1.4.3         boot_1.3-24          estimability_1.3     effectsize_0.1.1     codetools_0.2-16     reprex_0.3.0         glue_1.3.1          
[71] evaluate_0.14        modelr_0.1.5         operator.tools_1.6.3 vctrs_0.2.2          nloptr_1.2.1         cellranger_1.1.0     gtable_0.3.0        
[78] assertthat_0.2.1     xfun_0.12            xtable_1.8-4         broom_0.5.4          coda_0.19-3          survival_3.1-8       TH.data_1.0-10      

References

#' ---
#' title: "Apply Mixed Effect Models to Extended Modelling Input Data"
#' output: 
#'   html_document:
#'   toc: true
#'   toc_float: true
#'   toc_collapsed: true
#' toc_depth: 3
#' number_sections: true
#' theme: lumen
#' ---

#' # Preamble
#' 
#' This code commentary is included in the R code itself and can be rendered at
#' any stage using `rmarkdown::render ("/Users/paul/Documents/CU_combined/Github/500_83_get_mixed_effect_model_results.R", clean = TRUE, output_format = "html_notebook")`.
#' Please check the session info at the end of the document for further 
#' notes on the coding environment.
#' 
#' # Environment preparation
#'
#' Empty buffer.

rm(list=ls())

#' Load Packages

library ("tidyverse") # dplyr and friends
library ("ggplot2")   # for ggCaterpillar
library ("gdata")     # matrix functions
library ("reshape2")  # melting
library ("lme4")      # mixed effect model
library ("sjPlot")    # mixed effect model - with plotting
library ("cowplot")   # exporting ggplots
library ("formula.tools") # better formatting of formulas
library ("stringr")    # better string concatenation
library ("magrittr")  # back-piping (only used for type conversion)
#' Functions

# Loaded from helper script:
source("/Users/paul/Documents/CU_combined/Github/500_00_functions.R")

#' "Not in" function
`%!in%` = Negate(`%in%`)

#' Function to subset data to fit model variables. Currently there are more 
#' incomplete cases among Notre-Dame predictors then among Cornell predictors.
#' Consider running an extra analysis \n with Cornell data trimmed so as to match Notre Dame data.
match_data_to_formula <- function (formula_item, data_item){
  
  # package loading
  require ("tidyverse")
  
  # message
  message("\nData is subset to fit model variables, but currently there are more incomplete cases among Notre-Dame predictors then among Cornell predictors. Consider running an extra analysis with Cornell data trimmed so as to match Notre Dame data.")
  
  # Setting types
  #   for debugging only
  # print(head(data_item))
  
  message("- Setting types.")
  cols <- c("PORT", "DEST", "ECO_PORT", "ECO_DEST", "ECO_DIFF")
  data_item[cols] <- lapply(data_item[cols], as.factor)  

  #   for debugging only
  # print(head(data_item))
  
        
  # remove superflous columns
  vars_to_keep <- all.vars (formula_item)

  message("- Input dimensions are: ", paste0( (dim(data_item)), " "),  ".")
  message("- Removed variables are: ", paste0( names(data_item)[which(names(data_item) %!in% vars_to_keep)], " "), ".")
  message("- Kept variables are: ", paste0(vars_to_keep, " "), ".")
  
  data_item <- data_item %>% select(all_of(vars_to_keep))

  message("- Intermediate dimensions are: ", paste0( (dim(data_item)), " "), ".")
  
  # remove superflous rows
  message("- Undefined rows have been removed, assuming they were real \"NA\" and not \"0\".")
  
  data_item <- data_item %>% filter(complete.cases(.))
  
  message("- Final dimensions are: ", paste0( (dim(data_item)), " "), ".")
  
  # return table object suitable for modelling with model formula
  return(data_item)

}

#' Calculate random effect model results
calculate_model <- function(formula_item, data_item){
  
  message("\nModelling function received variables: ", paste0(names(data_item) , " "), ".")
  message("   ... dimensions: ", paste0( (dim(data_item)), " "), ".")
  message("   ... formula: ", paste0(formula_item , " "), "." )
  
  model <- lmer(formula_item, data = data_item, REML=FALSE)

  return(model)
}

#' # Model definitions
#' 
#' ##  Define full models
#'
#' following `https://stackoverflow.com/questions/25312818/using-lapply-to-fit-multiple-model-how-to-keep-the-model-formula-self-contain`

full_formulae <- list(
  
  # Original by Paul 
  as.formula(RESP_UNIFRAC ~ PRED_TRIPS + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)),
  
  # as per email 04.02.2020
  # Unifrac ~ VOY_FREQ + env similarity + ecoregion + random port effects
  as.formula(RESP_UNIFRAC ~ VOY_FREQ + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)),
   
  # Unifrac ~ B_FON_NOECO + env similarity + ecoregion + random port effects
  as.formula(RESP_UNIFRAC ~ B_FON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)),
  
  # Unifrac ~ B_HON_NOECO + env similarity + ecoregion + random port effects
  as.formula(RESP_UNIFRAC ~ B_HON_NOECO + PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST))
)

#' 
#' ##  Define null models
#'
#' For Anova comparison. Order *must* be the same as in list `full_models`.

null_formulae <- list(
  
  # Original by Paul 
  as.formula(RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)),
  
  # as per email 04.02.2020
  # Unifrac ~ VOY_FREQ + env similarity + ecoregion + random port effects
  as.formula(RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)),
   
  # Unifrac ~ B_FON_NOECO + env similarity + ecoregion + random port effects
  as.formula(RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST)),
  
  # Unifrac ~ B_HON_NOECO + env similarity + ecoregion + random port effects
  as.formula(RESP_UNIFRAC ~ PRED_ENV + ECO_DIFF + (1 | PORT) + (1 | DEST))
)

#' # Read in and format data
#'
#' Please refer to project README.md file for further details on previous processing steps (dated 31-Jan-2020). 

# define file path components for listing 
model_input_folder <- "/Users/paul/Documents/CU_combined/Zenodo/Results"
model_input_pattern <- glob2rx("??_results_euk_*_model_data_*.csv")

# read all file into lists for `lapply()` usage
model_input_files <- list.files(path=model_input_folder, 
  pattern = model_input_pattern, full.names = TRUE)

# store all tables in list and save input filenames alongside - skipping "X1" 
#  in case previous tables have column numbers, which they should not have anymore.
model_input_data <- suppressWarnings(lapply(model_input_files, 
  function(listed_file)  read_csv(listed_file, col_types = cols('X1' = col_skip()))))
names(model_input_data) <- model_input_files

#' # Obtaining modelling results
#'
#' ## Initialize results table
#' 
#' So that it can be filled in the loop.

analysis_summaries <- expand.grid(seq(model_input_data), seq(full_formulae))
analysis_summaries <- as_tibble(analysis_summaries)
analysis_summaries <- setNames(analysis_summaries, c("DIDX", "FIDX"))
analysis_summaries <- analysis_summaries %>% add_column(AKAI = 0, PVAL = 0, FRML = 0, DATA = 0)

analysis_summaries$AKAI  %<>% as.double
analysis_summaries$DATA %<>% as.character
analysis_summaries$FRML  %<>% as.character
analysis_summaries$PVAL  %<>% as.double

# use this approach to get around the loop - later
#   define all possible combinations for mapply call
#   for later - starting point
#   analysis_combinations <- expand.grid(seq(model_input_data), seq(full_formulae))
#   setNames(analysis_combinations, c("model_index", "formula_index"))
#   for later - starting point
#   list(model_input_data, full_formulae)

#'
#' ## Calculating Results
#' 
#' Initially using loops, for sanity reasons. While looping fill results table
#' `analysis_summaries`. 
#' Check raw model outputs below for `Writing above results to results table row: n` and look up `n` in both results tables all the way at the end of this page.

# loop over formulae
for (i in seq(full_formulae)){
  
  # loop over dat sets
  for (j in seq(model_input_data)){
  
    message("°º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ °º¤ø,¸¸,ø¤º°`°º¤ø,¸,ø¤°º¤ø,¸¸,ø¤º°`°º¤ø,¸ ")
    message("\nStarting new analysis, with data index DIDX \"", j , "\" and formula index FIDX \"", i, "\" in Summary Tables." ) 
    message("Using formula: ", as.character(full_formulae[[i]]), " with data: ", as.character(basename(names(model_input_data)[[j]])), ". ")

    # define current model formula for parsing
    full_formula <- full_formulae[[i]]
    null_formula <- null_formulae[[i]]
     
    # define current data table for subsetting
    model_data_raw <- model_input_data[[j]]
         
    # match input table dimensions to current model formulae
    model_data <- match_data_to_formula(full_formula, model_data_raw)
    print(model_data, n = Inf)
  
    # calculate full model
    full_model <- calculate_model(full_formula, model_data)
     
    # calculate null model
    null_model <- calculate_model(null_formula, model_data)
     
    # print model summary and evaluations
    message("\nGetting Model Summary: ")
    sm <- summary(full_model)
    print(sm)
    message("\nGetting Model Coefficients from Summary: ")
    print(sm$coefficients)
      
    message("\nGetting Model ANOVA: ")
    an <- try(anova(null_model, full_model))
    try(print(an))

    # plot model coefficients
    message("\nPlotting Model Coefficients: ")
    plot <- plot_model(full_model, show.values = TRUE, value.offset = .3,
     type = "std", 
     title = paste("Coefficients for formula \"", as.character(full_formula),
     "\" and variables \"", str_c(names(model_data), collapse = "\", \""),"\" of input file: \"",
    basename(names(model_input_data)[[j]]), "\"." ))
  
    print(plot)
  
    # gather results
    #   set current row of results table
    crnt_row <- intersect(which(analysis_summaries$DIDX == j), which(analysis_summaries$FIDX == i))
    # message("Writing above results to results table row (but the table is re-sorted): ", crnt_row)
  
    #    fill results table
    analysis_summaries[crnt_row, ]$AKAI <- extractAIC(full_model)[2]
    analysis_summaries[crnt_row, ]$DATA <- as.character(basename(names(model_input_data)[[j]]))
    analysis_summaries[crnt_row, ]$FRML <- as.character(full_formulae[[i]])
    analysis_summaries[crnt_row, ]$PVAL <- an[2,8]
  
    # keep in mind for further elements from anova object:
    #  > str(an)
    #  Classes ‘anova’ and 'data.frame':	2 obs. of  8 variables:
    #  $ Df        : num  6 7
    #  $ AIC       : num  -158 -159
    #  $ BIC       : num  -145 -144
    #  $ logLik    : num  84.8 86.5
    #  $ deviance  : num  -170 -173
    #  $ Chisq     : num  NA 3.49
    #  $ Chi Df    : num  NA 1
    #  $ Pr(>Chisq): num  NA 0.0617

  }
}

#' # Show Results table
#'
#' Check above raw model out put for `Writing above results to results table row: n` and look up `n` in both tables below.

#'
#' Sort results table by AIC

analysis_summaries <- arrange(analysis_summaries, AKAI)

#' Show results table interactively:

analysis_summaries

#' Show results table on screen:

print(analysis_summaries, n = Inf)

#' # On warning ?`isSingular`
#' 
#'  Complex mixed-effect models (i.e., those with
#' a large number of variance-covariance
#' parameters) frequently result in singular fits,
#' i.e. estimated variance-covariance matrices
#' with less than full rank. Less technically,
#' this means that some "dimensions" of the
#' variance-covariance matrix have been estimated
#' as exactly zero. For scalar random effects such
#' as intercept-only models, or 2-dimensional
#' random effects such as intercept+slope models,
#' singularity is relatively easy to detect
#' because it leads to random-effect variance
#' estimates of (nearly) zero, or estimates of
#' correlations that are (almost) exactly -1 or 1.
#' However, for more complex models
#' (variance-covariance matrices of dimension >=3)
#' singularity can be hard to detect; models can
#' often be singular without any of their
#' individual variances being close to zero or
#' correlations being close to +/-1.
#' 
#'   This function performs a simple test to
#' determine whether any of the random effects
#' covariance matrices of a fitted model are
#' singular. The rePCA method provides more detail
#' about the singularity pattern, showing the
#' standard deviations of orthogonal variance
#' components and the mapping from variance terms
#' in the model to orthogonal components (i.e.,
#' eigenvector/rotation matrices).
#' 
#'   While singular models are statistically well
#' defined (it is theoretically sensible for the
#' true maximum likelihood estimate to correspond
#' to a singular fit), there are real concerns
#' that (1) singular fits correspond to overfitted
#' models that may have poor power; (2) chances of
#' numerical problems and mis-convergence are
#' higher for singular models (e.g. it may be
#' computationally difficult to compute profile
#' confidence intervals for such models); (3)
#' standard inferential procedures such as Wald
#' statistics and likelihood ratio tests may be
#' inappropriate.
#' 
#'   There is not yet consensus about how to deal
#' with singularity, or more generally to choose
#' which random-effects specification (from a
#' range of choices of varying complexity) to use.
#' Some proposals include:
#' 
#'   avoid fitting overly complex models in the
#' first place, i.e. design experiments/restrict
#' models a priori such that the
#' variance-covariance matrices can be estimated
#' precisely enough to avoid singularity
#' (Matuschek et al 2017)
#' 
#'   use some form of model selection to choose a
#' model that balances predictive accuracy and
#' overfitting/type I error (Bates et al 2015,
#' Matuschek et al 2017)
#' 
#'   “keep it maximal”, i.e. fit the most complex
#' model consistent with the experimental design,
#' removing only terms required to allow a
#' non-singular fit (Barr et al. 2013), or
#' removing further terms based on p-values or AIC
#' 
#'   use a partially Bayesian method that produces
#' maximum a posteriori (MAP) estimates using
#' regularizing priors to force the estimated
#' random-effects variance-covariance matrices
#' away from singularity (Chung et al 2013, blme
#' package)
#' 
#'   use a fully Bayesian method that both
#' regularizes the model via informative priors
#' and gives estimates and credible intervals for
#' all parameters that average over the
#' uncertainty in the random effects parameters
#' (Gelman and Hill 2006, McElreath 2015;
#' MCMCglmm, rstanarm and brms packages)

#' # Session info
#'
#' The code and output in this document were tested and generated in the
#' following computing environment:
#+ echo=FALSE
sessionInfo()

#' # References
